Python Notes for Professionals. 800+ pages of professional hints and tricks

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Contents About ................................................................................................................................................................................... 1 Chapter 1: Getting started with Python Language ...................................................................................... 2 Section 1.1: Getting Started ........................................................................................................................................... 2 Section 1.2: Creating variables and assigning values ................................................................................................ 6 Section 1.3: Block Indentation ..................................................................................................................................... 10 Section 1.4: Datatypes ................................................................................................................................................. 11 Section 1.5: Collection Types ...................................................................................................................................... 15 Section 1.6: IDLE - Python GUI .................................................................................................................................... 19 Section 1.7: User Input ................................................................................................................................................. 21 Section 1.8: Built in Modules and Functions .............................................................................................................. 21 Section 1.9: Creating a module ................................................................................................................................... 25 Section 1.10: Installation of Python 2.7.x and 3.x ....................................................................................................... 26 Section 1.11: String function - str() and repr() ........................................................................................................... 28 Section 1.12: Installing external modules using pip ................................................................................................... 29 Section 1.13: Help Utility ............................................................................................................................................... 31

Chapter 2: Python Data Types ............................................................................................................................ 33 Section 2.1: String Data Type ..................................................................................................................................... 33 Section 2.2: Set Data Types ....................................................................................................................................... 33 Section 2.3: Numbers data type ................................................................................................................................ 33 Section 2.4: List Data Type ......................................................................................................................................... 34 Section 2.5: Dictionary Data Type ............................................................................................................................. 34 Section 2.6: Tuple Data Type ..................................................................................................................................... 34

Chapter 3: Indentation ............................................................................................................................................. 35 Section 3.1: Simple example ....................................................................................................................................... 35 Section 3.2: How Indentation is Parsed ..................................................................................................................... 35 Section 3.3: Indentation Errors ................................................................................................................................... 36

Chapter 4: Comments and Documentation .................................................................................................. 37 Section 4.1: Single line, inline and multiline comments ............................................................................................ 37 Section 4.2: Programmatically accessing docstrings .............................................................................................. 37 Section 4.3: Write documentation using docstrings ................................................................................................ 38

Chapter 5: Date and Time ...................................................................................................................................... 42 Section 5.1: Parsing a string into a timezone aware datetime object .................................................................... 42 Section 5.2: Constructing timezone-aware datetimes ............................................................................................ 42 Section 5.3: Computing time dierences .................................................................................................................. 44 Section 5.4: Basic datetime objects usage ............................................................................................................... 44 Section 5.5: Switching between time zones .............................................................................................................. 45 Section 5.6: Simple date arithmetic ........................................................................................................................... 45 Section 5.7: Converting timestamp to datetime ...................................................................................................... 46 Section 5.8: Subtracting months from a date accurately ....................................................................................... 46 Section 5.9: Parsing an arbitrary ISO 8601 timestamp with minimal libraries ...................................................... 46 Section 5.10: Get an ISO 8601 timestamp .................................................................................................................. 47 Section 5.11: Parsing a string with a short time zone name into a timezone aware datetime object ................ 47 Section 5.12: Fuzzy datetime parsing (extracting datetime out of a text) ............................................................ 48 Section 5.13: Iterate over dates .................................................................................................................................. 49

Chapter 6: Date Formatting .................................................................................................................................. 50 Section 6.1: Time between two date-times ............................................................................................................... 50 Section 6.2: Outputting datetime object to string .................................................................................................... 50

Section 6.3: Parsing string to datetime object ......................................................................................................... 50

Chapter 7: Enum .......................................................................................................................................................... 51 Section 7.1: Creating an enum (Python 2.4 through 3.3) ......................................................................................... 51 Section 7.2: Iteration ................................................................................................................................................... 51

Chapter 8: Set ............................................................................................................................................................... 52 Section 8.1: Operations on sets .................................................................................................................................. 52 Section 8.2: Get the unique elements of a list .......................................................................................................... 53 Section 8.3: Set of Sets ................................................................................................................................................ 53 Section 8.4: Set Operations using Methods and Builtins ......................................................................................... 53 Section 8.5: Sets versus multisets .............................................................................................................................. 55

Chapter 9: Simple Mathematical Operators ................................................................................................. 57 Section 9.1: Division ..................................................................................................................................................... 57 Section 9.2: Addition .................................................................................................................................................... 58 Section 9.3: Exponentiation ........................................................................................................................................ 59 Section 9.4: Trigonometric Functions ........................................................................................................................ 60 Section 9.5: Inplace Operations ................................................................................................................................. 61 Section 9.6: Subtraction .............................................................................................................................................. 61 Section 9.7: Multiplication ........................................................................................................................................... 61 Section 9.8: Logarithms .............................................................................................................................................. 62 Section 9.9: Modulus ................................................................................................................................................... 62

Chapter 10: Bitwise Operators ............................................................................................................................. 65 Section 10.1: Bitwise NOT ............................................................................................................................................ 65 Section 10.2: Bitwise XOR (Exclusive OR) .................................................................................................................. 66 Section 10.3: Bitwise AND ............................................................................................................................................ 67 Section 10.4: Bitwise OR .............................................................................................................................................. 67 Section 10.5: Bitwise Left Shift .................................................................................................................................... 67 Section 10.6: Bitwise Right Shift .................................................................................................................................. 68 Section 10.7: Inplace Operations ................................................................................................................................ 68

Chapter 11: Boolean Operators ............................................................................................................................ 69 Section 11.1: `and` and `or` are not guaranteed to return a boolean ...................................................................... 69 Section 11.2: A simple example ................................................................................................................................... 69 Section 11.3: Short-circuit evaluation ......................................................................................................................... 69 Section 11.4: and ........................................................................................................................................................... 70 Section 11.5: or .............................................................................................................................................................. 70 Section 11.6: not ............................................................................................................................................................ 71

Chapter 12: Operator Precedence ...................................................................................................................... 72 Section 12.1: Simple Operator Precedence Examples in python ............................................................................. 72

Chapter 13: Variable Scope and Binding ......................................................................................................... 73 Section 13.1: Nonlocal Variables ................................................................................................................................. 73 Section 13.2: Global Variables .................................................................................................................................... 73 Section 13.3: Local Variables ...................................................................................................................................... 74 Section 13.4: The del command ................................................................................................................................. 75 Section 13.5: Functions skip class scope when looking up names ......................................................................... 76 Section 13.6: Local vs Global Scope ........................................................................................................................... 77 Section 13.7: Binding Occurrence ............................................................................................................................... 79

Chapter 14: Conditionals ......................................................................................................................................... 80 Section 14.1: Conditional Expression (or "The Ternary Operator") ......................................................................... 80 Section 14.2: if, elif, and else ....................................................................................................................................... 80 Section 14.3: Truth Values ........................................................................................................................................... 80

Section 14.4: Boolean Logic Expressions ................................................................................................................... 81 Section 14.5: Using the cmp function to get the comparison result of two objects ............................................. 83 Section 14.6: Else statement ....................................................................................................................................... 83 Section 14.7: Testing if an object is None and assigning it ...................................................................................... 84 Section 14.8: If statement ............................................................................................................................................ 84

Chapter 15: Comparisons ........................................................................................................................................ 86 Section 15.1: Chain Comparisons ................................................................................................................................ 86 Section 15.2: Comparison by `is` vs `==` ...................................................................................................................... 87 Section 15.3: Greater than or less than ...................................................................................................................... 88 Section 15.4: Not equal to ........................................................................................................................................... 88 Section 15.5: Equal To ................................................................................................................................................. 89 Section 15.6: Comparing Objects ............................................................................................................................... 89

Chapter 16: Loops ....................................................................................................................................................... 91 Section 16.1: Break and Continue in Loops ................................................................................................................ 91 Section 16.2: For loops ................................................................................................................................................ 93 Section 16.3: Iterating over lists .................................................................................................................................. 93 Section 16.4: Loops with an "else" clause .................................................................................................................. 94 Section 16.5: The Pass Statement .............................................................................................................................. 96 Section 16.6: Iterating over dictionaries .................................................................................................................... 97 Section 16.7: The "half loop" do-while ........................................................................................................................ 98 Section 16.8: Looping and Unpacking ....................................................................................................................... 98 Section 16.9: Iterating dierent portion of a list with dierent step size ............................................................... 99 Section 16.10: While Loop .......................................................................................................................................... 100

Chapter 17: Arrays .................................................................................................................................................... 102 Section 17.1: Access individual elements through indexes ..................................................................................... 102 Section 17.2: Basic Introduction to Arrays .............................................................................................................. 102 Section 17.3: Append any value to the array using append() method ................................................................ 103 Section 17.4: Insert value in an array using insert() method ................................................................................ 103 Section 17.5: Extend python array using extend() method ................................................................................... 103 Section 17.6: Add items from list into array using fromlist() method .................................................................. 104 Section 17.7: Remove any array element using remove() method ..................................................................... 104 Section 17.8: Remove last array element using pop() method ............................................................................ 104 Section 17.9: Fetch any element through its index using index() method ........................................................... 104 Section 17.10: Reverse a python array using reverse() method ........................................................................... 104 Section 17.11: Get array buer information through buer_info() method ........................................................ 105 Section 17.12: Check for number of occurrences of an element using count() method .................................... 105 Section 17.13: Convert array to string using tostring() method ............................................................................ 105 Section 17.14: Convert array to a python list with same elements using tolist() method .................................. 105 Section 17.15: Append a string to char array using fromstring() method ........................................................... 105

Chapter 18: Multidimensional arrays .............................................................................................................. 106 Section 18.1: Lists in lists ............................................................................................................................................ 106 Section 18.2: Lists in lists in lists in.. .......................................................................................................................... 106

Chapter 19: Dictionary ............................................................................................................................................ 108 Section 19.1: Introduction to Dictionary ................................................................................................................... 108 Section 19.2: Avoiding KeyError Exceptions ........................................................................................................... 109 Section 19.3: Iterating Over a Dictionary ................................................................................................................. 109 Section 19.4: Dictionary with default values ........................................................................................................... 110 Section 19.5: Merging dictionaries ........................................................................................................................... 111 Section 19.6: Accessing keys and values ................................................................................................................ 111 Section 19.7: Accessing values of a dictionary ....................................................................................................... 112

Section 19.8: Creating a dictionary .......................................................................................................................... 112 Section 19.9: Creating an ordered dictionary ......................................................................................................... 113 Section 19.10: Unpacking dictionaries using the ** operator ................................................................................. 113 Section 19.11: The trailing comma ............................................................................................................................ 114 Section 19.12: The dict() constructor ........................................................................................................................ 114 Section 19.13: Dictionaries Example ......................................................................................................................... 114 Section 19.14: All combinations of dictionary values .............................................................................................. 115

Chapter 20: List ......................................................................................................................................................... 117 Section 20.1: List methods and supported operators ............................................................................................ 117 Section 20.2: Accessing list values .......................................................................................................................... 122 Section 20.3: Checking if list is empty ..................................................................................................................... 123 Section 20.4: Iterating over a list ............................................................................................................................. 123 Section 20.5: Checking whether an item is in a list ................................................................................................ 124 Section 20.6: Any and All .......................................................................................................................................... 124 Section 20.7: Reversing list elements ...................................................................................................................... 125 Section 20.8: Concatenate and Merge lists ............................................................................................................ 125 Section 20.9: Length of a list .................................................................................................................................... 126 Section 20.10: Remove duplicate values in list ....................................................................................................... 126 Section 20.11: Comparison of lists ............................................................................................................................ 127 Section 20.12: Accessing values in nested list ........................................................................................................ 127 Section 20.13: Initializing a List to a Fixed Number of Elements ........................................................................... 128

Chapter 21: List comprehensions ...................................................................................................................... 130 Section 21.1: List Comprehensions ........................................................................................................................... 130 Section 21.2: Conditional List Comprehensions ...................................................................................................... 132 Section 21.3: Avoid repetitive and expensive operations using conditional clause ............................................ 134 Section 21.4: Dictionary Comprehensions ............................................................................................................... 135 Section 21.5: List Comprehensions with Nested Loops .......................................................................................... 136 Section 21.6: Generator Expressions ........................................................................................................................ 138 Section 21.7: Set Comprehensions ........................................................................................................................... 140 Section 21.8: Refactoring filter and map to list comprehensions ......................................................................... 140 Section 21.9: Comprehensions involving tuples ...................................................................................................... 141 Section 21.10: Counting Occurrences Using Comprehension ............................................................................... 142 Section 21.11: Changing Types in a List .................................................................................................................... 142 Section 21.12: Nested List Comprehensions ............................................................................................................ 142 Section 21.13: Iterate two or more list simultaneously within list comprehension .............................................. 143

Chapter 22: List slicing (selecting parts of lists) ....................................................................................... 144 Section 22.1: Using the third "step" argument ........................................................................................................ 144 Section 22.2: Selecting a sublist from a list ............................................................................................................ 144 Section 22.3: Reversing a list with slicing ................................................................................................................ 144 Section 22.4: Shifting a list using slicing .................................................................................................................. 144

Chapter 23: groupby() ............................................................................................................................................ 146 Section 23.1: Example 4 ............................................................................................................................................. 146 Section 23.2: Example 2 ............................................................................................................................................ 146 Section 23.3: Example 3 ............................................................................................................................................ 147

Chapter 24: Linked lists ......................................................................................................................................... 149 Section 24.1: Single linked list example ................................................................................................................... 149

Chapter 25: Linked List Node ............................................................................................................................. 154 Section 25.1: Write a simple Linked List Node in python ....................................................................................... 154

Chapter 26: Filter ...................................................................................................................................................... 155

Section 26.1: Basic use of filter ................................................................................................................................. 155 Section 26.2: Filter without function ........................................................................................................................ 155 Section 26.3: Filter as short-circuit check ............................................................................................................... 156 Section 26.4: Complementary function: filterfalse, ifilterfalse .............................................................................. 156

Chapter 27: Heapq ................................................................................................................................................... 158 Section 27.1: Largest and smallest items in a collection ....................................................................................... 158 Section 27.2: Smallest item in a collection .............................................................................................................. 158

Chapter 28: Tuple ..................................................................................................................................................... 160 Section 28.1: Tuple ..................................................................................................................................................... 160 Section 28.2: Tuples are immutable ........................................................................................................................ 161 Section 28.3: Packing and Unpacking Tuples ........................................................................................................ 161 Section 28.4: Built-in Tuple Functions ..................................................................................................................... 162 Section 28.5: Tuple Are Element-wise Hashable and Equatable ......................................................................... 163 Section 28.6: Indexing Tuples ................................................................................................................................... 164 Section 28.7: Reversing Elements ............................................................................................................................ 164

Chapter 29: Basic Input and Output ............................................................................................................... 165 Section 29.1: Using the print function ...................................................................................................................... 165 Section 29.2: Input from a File ................................................................................................................................. 165 Section 29.3: Read from stdin .................................................................................................................................. 167 Section 29.4: Using input() and raw_input() .......................................................................................................... 167 Section 29.5: Function to prompt user for a number ............................................................................................ 167 Section 29.6: Printing a string without a newline at the end ................................................................................. 168

Chapter 30: Files & Folders I/O ......................................................................................................................... 171 Section 30.1: File modes ............................................................................................................................................ 171 Section 30.2: Reading a file line-by-line .................................................................................................................. 172 Section 30.3: Iterate files (recursively) .................................................................................................................... 173 Section 30.4: Getting the full contents of a file ...................................................................................................... 173 Section 30.5: Writing to a file ................................................................................................................................... 174 Section 30.6: Check whether a file or path exists .................................................................................................. 175 Section 30.7: Random File Access Using mmap .................................................................................................... 176 Section 30.8: Replacing text in a file ....................................................................................................................... 176 Section 30.9: Checking if a file is empty ................................................................................................................. 176 Section 30.10: Read a file between a range of lines .............................................................................................. 177 Section 30.11: Copy a directory tree ........................................................................................................................ 177 Section 30.12: Copying contents of one file to a dierent file .............................................................................. 177

Chapter 31: os.path .................................................................................................................................................. 178 Section 31.1: Join Paths ............................................................................................................................................. 178 Section 31.2: Path Component Manipulation .......................................................................................................... 178 Section 31.3: Get the parent directory ..................................................................................................................... 178 Section 31.4: If the given path exists ........................................................................................................................ 178 Section 31.5: check if the given path is a directory, file, symbolic link, mount point etc .................................... 179 Section 31.6: Absolute Path from Relative Path ..................................................................................................... 179

Chapter 32: Iterables and Iterators ................................................................................................................ 180 Section 32.1: Iterator vs Iterable vs Generator ....................................................................................................... 180 Section 32.2: Extract values one by one ................................................................................................................. 181 Section 32.3: Iterating over entire iterable ............................................................................................................. 181 Section 32.4: Verify only one element in iterable .................................................................................................. 181 Section 32.5: What can be iterable .......................................................................................................................... 182 Section 32.6: Iterator isn't reentrant! ....................................................................................................................... 182

Chapter 33: Functions ............................................................................................................................................. 183 Section 33.1: Defining and calling simple functions ............................................................................................... 183 Section 33.2: Defining a function with an arbitrary number of arguments ........................................................ 184 Section 33.3: Lambda (Inline/Anonymous) Functions .......................................................................................... 187 Section 33.4: Defining a function with optional arguments .................................................................................. 189 Section 33.5: Defining a function with optional mutable arguments ................................................................... 190 Section 33.6: Argument passing and mutability .................................................................................................... 191 Section 33.7: Returning values from functions ....................................................................................................... 192 Section 33.8: Closure ................................................................................................................................................. 192 Section 33.9: Forcing the use of named parameters ............................................................................................ 193 Section 33.10: Nested functions ............................................................................................................................... 194 Section 33.11: Recursion limit .................................................................................................................................... 194 Section 33.12: Recursive Lambda using assigned variable ................................................................................... 195 Section 33.13: Recursive functions ........................................................................................................................... 195 Section 33.14: Defining a function with arguments ................................................................................................ 196 Section 33.15: Iterable and dictionary unpacking .................................................................................................. 196 Section 33.16: Defining a function with multiple arguments ................................................................................. 198

Chapter 34: Defining functions with list arguments .............................................................................. 199 Section 34.1: Function and Call ................................................................................................................................. 199

Chapter 35: Functional Programming in Python ...................................................................................... 201 Section 35.1: Lambda Function ................................................................................................................................ 201 Section 35.2: Map Function ...................................................................................................................................... 201 Section 35.3: Reduce Function ................................................................................................................................. 201 Section 35.4: Filter Function ..................................................................................................................................... 201

Chapter 36: Partial functions .............................................................................................................................. 202 Section 36.1: Raise the power ................................................................................................................................... 202

Chapter 37: Decorators ......................................................................................................................................... 203 Section 37.1: Decorator function .............................................................................................................................. 203 Section 37.2: Decorator class ................................................................................................................................... 204 Section 37.3: Decorator with arguments (decorator factory) .............................................................................. 205 Section 37.4: Making a decorator look like the decorated function .................................................................... 207 Section 37.5: Using a decorator to time a function ............................................................................................... 207 Section 37.6: Create singleton class with a decorator .......................................................................................... 208

Chapter 38: Classes ................................................................................................................................................. 209 Section 38.1: Introduction to classes ........................................................................................................................ 209 Section 38.2: Bound, unbound, and static methods .............................................................................................. 210 Section 38.3: Basic inheritance ................................................................................................................................ 212 Section 38.4: Monkey Patching ................................................................................................................................ 214 Section 38.5: New-style vs. old-style classes .......................................................................................................... 214 Section 38.6: Class methods: alternate initializers ................................................................................................. 215 Section 38.7: Multiple Inheritance ............................................................................................................................ 217 Section 38.8: Properties ............................................................................................................................................ 219 Section 38.9: Default values for instance variables ............................................................................................... 220 Section 38.10: Class and instance variables ........................................................................................................... 221 Section 38.11: Class composition .............................................................................................................................. 222 Section 38.12: Listing All Class Members ................................................................................................................. 223 Section 38.13: Singleton class ................................................................................................................................... 224 Section 38.14: Descriptors and Dotted Lookups .................................................................................................... 225

Chapter 39: Metaclasses ....................................................................................................................................... 226

Section 39.1: Basic Metaclasses ............................................................................................................................... 226 Section 39.2: Singletons using metaclasses ........................................................................................................... 227 Section 39.3: Using a metaclass .............................................................................................................................. 227 Section 39.4: Introduction to Metaclasses .............................................................................................................. 227 Section 39.5: Custom functionality with metaclasses ........................................................................................... 228 Section 39.6: The default metaclass ....................................................................................................................... 229

Chapter 40: String Formatting ......................................................................................................................... 232 Section 40.1: Basics of String Formatting ............................................................................................................... 232 Section 40.2: Alignment and padding ..................................................................................................................... 233 Section 40.3: Format literals (f-string) .................................................................................................................... 234 Section 40.4: Float formatting ................................................................................................................................. 234 Section 40.5: Named placeholders ......................................................................................................................... 235 Section 40.6: String formatting with datetime ....................................................................................................... 236 Section 40.7: Formatting Numerical Values ........................................................................................................... 236 Section 40.8: Nested formatting .............................................................................................................................. 237 Section 40.9: Format using Getitem and Getattr ................................................................................................... 237 Section 40.10: Padding and truncating strings, combined .................................................................................... 237 Section 40.11: Custom formatting for a class ......................................................................................................... 238

Chapter 41: String Methods ................................................................................................................................ 240 Section 41.1: Changing the capitalization of a string ............................................................................................. 240 Section 41.2: str.translate: Translating characters in a string ............................................................................... 241 Section 41.3: str.format and f-strings: Format values into a string ...................................................................... 242 Section 41.4: String module's useful constants ....................................................................................................... 243 Section 41.5: Stripping unwanted leading/trailing characters from a string ...................................................... 244 Section 41.6: Reversing a string ............................................................................................................................... 245 Section 41.7: Split a string based on a delimiter into a list of strings ................................................................... 245 Section 41.8: Replace all occurrences of one substring with another substring ................................................ 246 Section 41.9: Testing what a string is composed of ............................................................................................... 247 Section 41.10: String Contains ................................................................................................................................... 249 Section 41.11: Join a list of strings into one string ................................................................................................... 249 Section 41.12: Counting number of times a substring appears in a string .......................................................... 250 Section 41.13: Case insensitive string comparisons ................................................................................................ 250 Section 41.14: Justify strings ..................................................................................................................................... 251 Section 41.15: Test the starting and ending characters of a string ...................................................................... 252 Section 41.16: Conversion between str or bytes data and unicode characters .................................................. 253

Chapter 42: Using loops within functions .................................................................................................... 255 Section 42.1: Return statement inside loop in a function ...................................................................................... 255

Chapter 43: Importing modules ........................................................................................................................ 256 Section 43.1: Importing a module ............................................................................................................................ 256 Section 43.2: The __all__ special variable ............................................................................................................ 257 Section 43.3: Import modules from an arbitrary filesystem location .................................................................. 258 Section 43.4: Importing all names from a module ................................................................................................ 258 Section 43.5: Programmatic importing ................................................................................................................... 259 Section 43.6: PEP8 rules for Imports ....................................................................................................................... 259 Section 43.7: Importing specific names from a module ........................................................................................ 260 Section 43.8: Importing submodules ....................................................................................................................... 260 Section 43.9: Re-importing a module ...................................................................................................................... 260 Section 43.10: __import__() function ..................................................................................................................... 261

Chapter 44: Dierence between Module and Package ...................................................................... 262 Section 44.1: Modules ................................................................................................................................................ 262

Section 44.2: Packages ............................................................................................................................................. 262

Chapter 45: Math Module .................................................................................................................................... 264 Section 45.1: Rounding: round, floor, ceil, trunc ...................................................................................................... 264 Section 45.2: Trigonometry ...................................................................................................................................... 265 Section 45.3: Pow for faster exponentiation ........................................................................................................... 266 Section 45.4: Infinity and NaN ("not a number") ................................................................................................... 266 Section 45.5: Logarithms .......................................................................................................................................... 269 Section 45.6: Constants ............................................................................................................................................ 269 Section 45.7: Imaginary Numbers ........................................................................................................................... 270 Section 45.8: Copying signs ..................................................................................................................................... 270 Section 45.9: Complex numbers and the cmath module ...................................................................................... 270

Chapter 46: Complex math ................................................................................................................................. 273 Section 46.1: Advanced complex arithmetic ........................................................................................................... 273 Section 46.2: Basic complex arithmetic .................................................................................................................. 274

Chapter 47: Collections module ....................................................................................................................... 275 Section 47.1: collections.Counter .............................................................................................................................. 275 Section 47.2: collections.OrderedDict ...................................................................................................................... 276 Section 47.3: collections.defaultdict ......................................................................................................................... 277 Section 47.4: collections.namedtuple ...................................................................................................................... 278 Section 47.5: collections.deque ................................................................................................................................ 279 Section 47.6: collections.ChainMap .......................................................................................................................... 280

Chapter 48: Operator module ........................................................................................................................... 282 Section 48.1: Itemgetter ............................................................................................................................................ 282 Section 48.2: Operators as alternative to an infix operator ................................................................................. 282 Section 48.3: Methodcaller ....................................................................................................................................... 282

Chapter 49: JSON Module .................................................................................................................................... 284 Section 49.1: Storing data in a file ............................................................................................................................ 284 Section 49.2: Retrieving data from a file ................................................................................................................ 284 Section 49.3: Formatting JSON output ................................................................................................................... 284 Section 49.4: `load` vs `loads`, `dump` vs `dumps` ................................................................................................... 285 Section 49.5: Calling `json.tool` from the command line to pretty-print JSON output ...................................... 286 Section 49.6: JSON encoding custom objects ........................................................................................................ 286 Section 49.7: Creating JSON from Python dict ...................................................................................................... 287 Section 49.8: Creating Python dict from JSON ...................................................................................................... 287

Chapter 50: Sqlite3 Module ................................................................................................................................. 289 Section 50.1: Sqlite3 - Not require separate server process ................................................................................. 289 Section 50.2: Getting the values from the database and Error handling ........................................................... 289

Chapter 51: The os Module ................................................................................................................................... 291 Section 51.1: makedirs - recursive directory creation ............................................................................................ 291 Section 51.2: Create a directory ............................................................................................................................... 292 Section 51.3: Get current directory .......................................................................................................................... 292 Section 51.4: Determine the name of the operating system ................................................................................ 292 Section 51.5: Remove a directory ............................................................................................................................ 292 Section 51.6: Follow a symlink (POSIX) .................................................................................................................... 292 Section 51.7: Change permissions on a file ............................................................................................................. 292

Chapter 52: The locale Module .......................................................................................................................... 293 Section 52.1: Currency Formatting US Dollars Using the locale Module ............................................................. 293

Chapter 53: Itertools Module .............................................................................................................................. 294 Section 53.1: Combinations method in Itertools Module ....................................................................................... 294

Section 53.2: itertools.dropwhile .............................................................................................................................. 294 Section 53.3: Zipping two iterators until they are both exhausted ...................................................................... 295 Section 53.4: Take a slice of a generator ............................................................................................................... 295 Section 53.5: Grouping items from an iterable object using a function .............................................................. 296 Section 53.6: itertools.takewhile ............................................................................................................................... 297 Section 53.7: itertools.permutations ........................................................................................................................ 297 Section 53.8: itertools.repeat .................................................................................................................................... 298 Section 53.9: Get an accumulated sum of numbers in an iterable ...................................................................... 298 Section 53.10: Cycle through elements in an iterator ............................................................................................ 298 Section 53.11: itertools.product ................................................................................................................................. 298 Section 53.12: itertools.count .................................................................................................................................... 299 Section 53.13: Chaining multiple iterators together ............................................................................................... 300

Chapter 54: Asyncio Module ............................................................................................................................... 301 Section 54.1: Coroutine and Delegation Syntax ..................................................................................................... 301 Section 54.2: Asynchronous Executors ................................................................................................................... 302 Section 54.3: Using UVLoop ..................................................................................................................................... 303 Section 54.4: Synchronization Primitive: Event ....................................................................................................... 303 Section 54.5: A Simple Websocket .......................................................................................................................... 304 Section 54.6: Common Misconception about asyncio .......................................................................................... 304

Chapter 55: Random module ............................................................................................................................. 307 Section 55.1: Creating a random user password ................................................................................................... 307 Section 55.2: Create cryptographically secure random numbers ....................................................................... 307 Section 55.3: Random and sequences: shue, choice and sample .................................................................... 308 Section 55.4: Creating random integers and floats: randint, randrange, random, and uniform ...................... 309 Section 55.5: Reproducible random numbers: Seed and State ............................................................................ 310 Section 55.6: Random Binary Decision ................................................................................................................... 311

Chapter 56: Functools Module ........................................................................................................................... 312 Section 56.1: partial ................................................................................................................................................... 312 Section 56.2: cmp_to_key ....................................................................................................................................... 312 Section 56.3: lru_cache ............................................................................................................................................. 312 Section 56.4: total_ordering ..................................................................................................................................... 313 Section 56.5: reduce .................................................................................................................................................. 314

Chapter 57: The dis module ................................................................................................................................ 315 Section 57.1: What is Python bytecode? ................................................................................................................. 315 Section 57.2: Constants in the dis module .............................................................................................................. 315 Section 57.3: Disassembling modules ..................................................................................................................... 315

Chapter 58: The base64 Module ....................................................................................................................... 317 Section 58.1: Encoding and Decoding Base64 ....................................................................................................... 318 Section 58.2: Encoding and Decoding Base32 ....................................................................................................... 319 Section 58.3: Encoding and Decoding Base16 ........................................................................................................ 320 Section 58.4: Encoding and Decoding ASCII85 ...................................................................................................... 320 Section 58.5: Encoding and Decoding Base85 ....................................................................................................... 321

Chapter 59: Queue Module .................................................................................................................................. 322 Section 59.1: Simple example ................................................................................................................................... 322

Chapter 60: Deque Module .................................................................................................................................. 324 Section 60.1: Basic deque using ............................................................................................................................... 324 Section 60.2: Available methods in deque .............................................................................................................. 324 Section 60.3: limit deque size ................................................................................................................................... 325 Section 60.4: Breadth First Search .......................................................................................................................... 325

Chapter 61: Webbrowser Module ...................................................................................................................... 326 Section 61.1: Opening a URL with Default Browser ................................................................................................ 326 Section 61.2: Opening a URL with Dierent Browsers ........................................................................................... 327

Chapter 62: tkinter ................................................................................................................................................... 328 Section 62.1: Geometry Managers ........................................................................................................................... 328 Section 62.2: A minimal tkinter Application ............................................................................................................ 329

Chapter 63: pyautogui module .......................................................................................................................... 331 Section 63.1: Mouse Functions .................................................................................................................................. 331 Section 63.2: Keyboard Functions ........................................................................................................................... 331 Section 63.3: Screenshot And Image Recognition ................................................................................................. 331

Chapter 64: Indexing and Slicing ...................................................................................................................... 332 Section 64.1: Basic Slicing ......................................................................................................................................... 332 Section 64.2: Reversing an object ........................................................................................................................... 333 Section 64.3: Slice assignment ................................................................................................................................. 333 Section 64.4: Making a shallow copy of an array .................................................................................................. 333 Section 64.5: Indexing custom classes: __getitem__, __setitem__ and __delitem__ ................................... 334 Section 64.6: Basic Indexing ..................................................................................................................................... 335

Chapter 65: Plotting with Matplotlib .............................................................................................................. 337 Section 65.1: Plots with Common X-axis but dierent Y-axis : Using twinx() ....................................................... 337 Section 65.2: Plots with common Y-axis and dierent X-axis using twiny() ....................................................... 338 Section 65.3: A Simple Plot in Matplotlib ................................................................................................................. 340 Section 65.4: Adding more features to a simple plot : axis labels, title, axis ticks, grid, and legend ................ 341 Section 65.5: Making multiple plots in the same figure by superimposition similar to MATLAB ...................... 342 Section 65.6: Making multiple Plots in the same figure using plot superimposition with separate plot commands ......................................................................................................................................................... 343

Chapter 66: graph-tool .......................................................................................................................................... 345 Section 66.1: PyDotPlus ............................................................................................................................................. 345 Section 66.2: PyGraphviz .......................................................................................................................................... 345

Chapter 67: Generators ......................................................................................................................................... 347 Section 67.1: Introduction .......................................................................................................................................... 347 Section 67.2: Infinite sequences ............................................................................................................................... 349 Section 67.3: Sending objects to a generator ........................................................................................................ 350 Section 67.4: Yielding all values from another iterable ......................................................................................... 351 Section 67.5: Iteration ............................................................................................................................................... 351 Section 67.6: The next() function ............................................................................................................................. 351 Section 67.7: Coroutines ........................................................................................................................................... 352 Section 67.8: Refactoring list-building code ........................................................................................................... 352 Section 67.9: Yield with recursion: recursively listing all files in a directory ........................................................ 353 Section 67.10: Generator expressions ...................................................................................................................... 354 Section 67.11: Using a generator to find Fibonacci Numbers ............................................................................... 354 Section 67.12: Searching ........................................................................................................................................... 354 Section 67.13: Iterating over generators in parallel ............................................................................................... 355

Chapter 68: Reduce ................................................................................................................................................. 356 Section 68.1: Overview .............................................................................................................................................. 356 Section 68.2: Using reduce ....................................................................................................................................... 356 Section 68.3: Cumulative product ............................................................................................................................ 357 Section 68.4: Non short-circuit variant of any/all ................................................................................................. 357

Chapter 69: Map Function .................................................................................................................................... 358 Section 69.1: Basic use of map, itertools.imap and future_builtins.map ............................................................. 358

Section 69.2: Mapping each value in an iterable ................................................................................................... 358 Section 69.3: Mapping values of dierent iterables .............................................................................................. 359 Section 69.4: Transposing with Map: Using "None" as function argument (python 2.x only) .......................... 361 Section 69.5: Series and Parallel Mapping .............................................................................................................. 361

Chapter 70: Exponentiation ................................................................................................................................ 365 Section 70.1: Exponentiation using builtins: ** and pow() ...................................................................................... 365 Section 70.2: Square root: math.sqrt() and cmath.sqrt ......................................................................................... 365 Section 70.3: Modular exponentiation: pow() with 3 arguments .......................................................................... 366 Section 70.4: Computing large integer roots ......................................................................................................... 366 Section 70.5: Exponentiation using the math module: math.pow() ..................................................................... 367 Section 70.6: Exponential function: math.exp() and cmath.exp() ......................................................................... 368 Section 70.7: Exponential function minus 1: math.expm1() .................................................................................... 368 Section 70.8: Magic methods and exponentiation: builtin, math and cmath ...................................................... 369 Section 70.9: Roots: nth-root with fractional exponents ....................................................................................... 370

Chapter 71: Searching ............................................................................................................................................ 371 Section 71.1: Searching for an element .................................................................................................................... 371 Section 71.2: Searching in custom classes: __contains__ and __iter__ ........................................................... 371 Section 71.3: Getting the index for strings: str.index(), str.rindex() and str.find(), str.rfind() ............................... 372 Section 71.4: Getting the index list and tuples: list.index(), tuple.index() .............................................................. 373 Section 71.5: Searching key(s) for a value in dict ................................................................................................... 373 Section 71.6: Getting the index for sorted sequences: bisect.bisect_left() .......................................................... 374 Section 71.7: Searching nested sequences ............................................................................................................. 374

Chapter 72: Sorting, Minimum and Maximum ............................................................................................ 376 Section 72.1: Make custom classes orderable ........................................................................................................ 376 Section 72.2: Special case: dictionaries ................................................................................................................... 378 Section 72.3: Using the key argument .................................................................................................................... 379 Section 72.4: Default Argument to max, min ......................................................................................................... 379 Section 72.5: Getting a sorted sequence ................................................................................................................ 380 Section 72.6: Extracting N largest or N smallest items from an iterable ............................................................ 380 Section 72.7: Getting the minimum or maximum of several values .................................................................... 381 Section 72.8: Minimum and Maximum of a sequence ........................................................................................... 381

Chapter 73: Counting .............................................................................................................................................. 382 Section 73.1: Counting all occurrence of all items in an iterable: collections.Counter ........................................ 382 Section 73.2: Getting the most common value(-s): collections.Counter.most_common() ................................ 382 Section 73.3: Counting the occurrences of one item in a sequence: list.count() and tuple.count() .................. 382 Section 73.4: Counting the occurrences of a substring in a string: str.count() ................................................... 383 Section 73.5: Counting occurrences in numpy array ............................................................................................ 383

Chapter 74: The Print Function ......................................................................................................................... 384 Section 74.1: Print basics ........................................................................................................................................... 384 Section 74.2: Print parameters ................................................................................................................................ 385

Chapter 75: Regular Expressions (Regex) ................................................................................................... 388 Section 75.1: Matching the beginning of a string ................................................................................................... 388 Section 75.2: Searching ............................................................................................................................................ 389 Section 75.3: Precompiled patterns ......................................................................................................................... 389 Section 75.4: Flags .................................................................................................................................................... 390 Section 75.5: Replacing ............................................................................................................................................. 391 Section 75.6: Find All Non-Overlapping Matches ................................................................................................... 391 Section 75.7: Checking for allowed characters ...................................................................................................... 392 Section 75.8: Splitting a string using regular expressions ..................................................................................... 392 Section 75.9: Grouping .............................................................................................................................................. 392

Section 75.10: Escaping Special Characters ........................................................................................................... 393 Section 75.11: Match an expression only in specific locations ............................................................................... 394 Section 75.12: Iterating over matches using `re.finditer` ........................................................................................ 395

Chapter 76: Copying data .................................................................................................................................... 396 Section 76.1: Copy a dictionary ................................................................................................................................ 396 Section 76.2: Performing a shallow copy ............................................................................................................... 396 Section 76.3: Performing a deep copy .................................................................................................................... 396 Section 76.4: Performing a shallow copy of a list .................................................................................................. 396 Section 76.5: Copy a set ........................................................................................................................................... 396

Chapter 77: Context Managers (“with” Statement) ............................................................................... 398 Section 77.1: Introduction to context managers and the with statement ............................................................ 398 Section 77.2: Writing your own context manager ................................................................................................. 398 Section 77.3: Writing your own contextmanager using generator syntax ......................................................... 399 Section 77.4: Multiple context managers ................................................................................................................ 400 Section 77.5: Assigning to a target .......................................................................................................................... 400 Section 77.6: Manage Resources ............................................................................................................................. 401

Chapter 78: The __name__ special variable ........................................................................................... 402 Section 78.1: __name__ == '__main__' ................................................................................................................. 402 Section 78.2: Use in logging ..................................................................................................................................... 402 Section 78.3: function_class_or_module.__name__ .......................................................................................... 402

Chapter 79: Checking Path Existence and Permissions ......................................................................... 404 Section 79.1: Perform checks using os.access ........................................................................................................ 404

Chapter 80: Creating Python packages ....................................................................................................... 406 Section 80.1: Introduction ......................................................................................................................................... 406 Section 80.2: Uploading to PyPI .............................................................................................................................. 406 Section 80.3: Making package executable ............................................................................................................. 408

Chapter 81: Usage of "pip" module: PyPI Package Manager ............................................................. 410 Section 81.1: Example use of commands ................................................................................................................ 410 Section 81.2: Handling ImportError Exception ........................................................................................................ 410 Section 81.3: Force install .......................................................................................................................................... 411

Chapter 82: pip: PyPI Package Manager ...................................................................................................... 412 Section 82.1: Install Packages .................................................................................................................................. 412 Section 82.2: To list all packages installed using `pip` ........................................................................................... 412 Section 82.3: Upgrade Packages ............................................................................................................................ 412 Section 82.4: Uninstall Packages ............................................................................................................................. 413 Section 82.5: Updating all outdated packages on Linux ...................................................................................... 413 Section 82.6: Updating all outdated packages on Windows ................................................................................ 413 Section 82.7: Create a requirements.txt file of all packages on the system ....................................................... 413 Section 82.8: Using a certain Python version with pip .......................................................................................... 414 Section 82.9: Create a requirements.txt file of packages only in the current virtualenv .................................. 414 Section 82.10: Installing packages not yet on pip as wheels ................................................................................ 415

Chapter 83: Parsing Command Line arguments ...................................................................................... 418 Section 83.1: Hello world in argparse ...................................................................................................................... 418 Section 83.2: Using command line arguments with argv ..................................................................................... 418 Section 83.3: Setting mutually exclusive arguments with argparse .................................................................... 419 Section 83.4: Basic example with docopt ............................................................................................................... 420 Section 83.5: Custom parser error message with argparse ................................................................................. 420 Section 83.6: Conceptual grouping of arguments with argparse.add_argument_group() ............................. 421 Section 83.7: Advanced example with docopt and docopt_dispatch ................................................................. 422

Chapter 84: Subprocess Library ...................................................................................................................... 424 Section 84.1: More flexibility with Popen ................................................................................................................. 424 Section 84.2: Calling External Commands .............................................................................................................. 425 Section 84.3: How to create the command list argument .................................................................................... 425

Chapter 85: setup.py .............................................................................................................................................. 427 Section 85.1: Purpose of setup.py ............................................................................................................................ 427 Section 85.2: Using source control metadata in setup.py .................................................................................... 427 Section 85.3: Adding command line scripts to your python package ................................................................. 428 Section 85.4: Adding installation options ................................................................................................................ 428

Chapter 86: Recursion ............................................................................................................................................ 430 Section 86.1: The What, How, and When of Recursion .......................................................................................... 430 Section 86.2: Tree exploration with recursion ........................................................................................................ 433 Section 86.3: Sum of numbers from 1 to n .............................................................................................................. 434 Section 86.4: Increasing the Maximum Recursion Depth ...................................................................................... 434 Section 86.5: Tail Recursion - Bad Practice ............................................................................................................ 435 Section 86.6: Tail Recursion Optimization Through Stack Introspection ............................................................ 435

Chapter 87: Type Hints .......................................................................................................................................... 437 Section 87.1: Adding types to a function ................................................................................................................. 437 Section 87.2: NamedTuple ....................................................................................................................................... 438 Section 87.3: Generic Types ..................................................................................................................................... 438 Section 87.4: Variables and Attributes .................................................................................................................... 438 Section 87.5: Class Members and Methods ............................................................................................................ 439 Section 87.6: Type hints for keyword arguments .................................................................................................. 439

Chapter 88: Exceptions .......................................................................................................................................... 440 Section 88.1: Catching Exceptions ............................................................................................................................ 440 Section 88.2: Do not catch everything! ................................................................................................................... 440 Section 88.3: Re-raising exceptions ......................................................................................................................... 441 Section 88.4: Catching multiple exceptions ............................................................................................................ 441 Section 88.5: Exception Hierarchy ........................................................................................................................... 442 Section 88.6: Else ....................................................................................................................................................... 444 Section 88.7: Raising Exceptions .............................................................................................................................. 444 Section 88.8: Creating custom exception types ..................................................................................................... 445 Section 88.9: Practical examples of exception handling ....................................................................................... 445 Section 88.10: Exceptions are Objects too .............................................................................................................. 446 Section 88.11: Running clean-up code with finally .................................................................................................. 446 Section 88.12: Chain exceptions with raise from .................................................................................................... 447

Chapter 89: Raise Custom Errors / Exceptions ......................................................................................... 448 Section 89.1: Custom Exception ............................................................................................................................... 448 Section 89.2: Catch custom Exception .................................................................................................................... 448

Chapter 90: Commonwealth Exceptions ....................................................................................................... 450 Section 90.1: Other Errors ......................................................................................................................................... 450 Section 90.2: NameError: name '???' is not defined .............................................................................................. 451 Section 90.3: TypeErrors .......................................................................................................................................... 452 Section 90.4: Syntax Error on good code ............................................................................................................... 453 Section 90.5: IndentationErrors (or indentation SyntaxErrors) ............................................................................ 454

Chapter 91: urllib ....................................................................................................................................................... 456 Section 91.1: HTTP GET .............................................................................................................................................. 456 Section 91.2: HTTP POST .......................................................................................................................................... 456 Section 91.3: Decode received bytes according to content type encoding ........................................................ 457

Chapter 92: Web scraping with Python ......................................................................................................... 458 Section 92.1: Scraping using the Scrapy framework ............................................................................................. 458 Section 92.2: Scraping using Selenium WebDriver ................................................................................................ 458 Section 92.3: Basic example of using requests and lxml to scrape some data ................................................. 459 Section 92.4: Maintaining web-scraping session with requests ........................................................................... 459 Section 92.5: Scraping using BeautifulSoup4 ......................................................................................................... 460 Section 92.6: Simple web content download with urllib.request .......................................................................... 460 Section 92.7: Modify Scrapy user agent ................................................................................................................. 460 Section 92.8: Scraping with curl ............................................................................................................................... 460

Chapter 93: HTML Parsing .................................................................................................................................... 462 Section 93.1: Using CSS selectors in BeautifulSoup ................................................................................................ 462 Section 93.2: PyQuery ............................................................................................................................................... 462 Section 93.3: Locate a text after an element in BeautifulSoup ............................................................................ 463

Chapter 94: Manipulating XML .......................................................................................................................... 464 Section 94.1: Opening and reading using an ElementTree ................................................................................... 464 Section 94.2: Create and Build XML Documents .................................................................................................... 464 Section 94.3: Modifying an XML File ........................................................................................................................ 465 Section 94.4: Searching the XML with XPath .......................................................................................................... 465 Section 94.5: Opening and reading large XML files using iterparse (incremental parsing) ............................. 466

Chapter 95: Python Requests Post .................................................................................................................. 468 Section 95.1: Simple Post .......................................................................................................................................... 468 Section 95.2: Form Encoded Data ........................................................................................................................... 469 Section 95.3: File Upload .......................................................................................................................................... 469 Section 95.4: Responses ........................................................................................................................................... 470 Section 95.5: Authentication ..................................................................................................................................... 470 Section 95.6: Proxies ................................................................................................................................................. 471

Chapter 96: Distribution ........................................................................................................................................ 473 Section 96.1: py2app ................................................................................................................................................. 473 Section 96.2: cx_Freeze ............................................................................................................................................ 474

Chapter 97: Property Objects ............................................................................................................................ 475 Section 97.1: Using the @property decorator for read-write properties ............................................................. 475 Section 97.2: Using the @property decorator ....................................................................................................... 475 Section 97.3: Overriding just a getter, setter or a deleter of a property object ................................................. 476 Section 97.4: Using properties without decorators ................................................................................................ 476

Chapter 98: Overloading ...................................................................................................................................... 479 Section 98.1: Operator overloading ......................................................................................................................... 479 Section 98.2: Magic/Dunder Methods .................................................................................................................... 480 Section 98.3: Container and sequence types ......................................................................................................... 481 Section 98.4: Callable types ..................................................................................................................................... 482 Section 98.5: Handling unimplemented behaviour ................................................................................................ 482

Chapter 99: Polymorphism .................................................................................................................................. 484 Section 99.1: Duck Typing ......................................................................................................................................... 484 Section 99.2: Basic Polymorphism .......................................................................................................................... 484

Chapter 100: Method Overriding ...................................................................................................................... 488 Section 100.1: Basic method overriding ................................................................................................................... 488

Chapter 101: User-Defined Methods ................................................................................................................ 489 Section 101.1: Creating user-defined method objects ............................................................................................ 489 Section 101.2: Turtle example ................................................................................................................................... 490

Chapter 102: String representations of class instances: __str__ and __repr__

methods ........................................................................................................................................................................ 491 Section 102.1: Motivation ........................................................................................................................................... 491 Section 102.2: Both methods implemented, eval-round-trip style __repr__() .................................................. 495

Chapter 103: Debugging ........................................................................................................................................ 496 Section 103.1: Via IPython and ipdb ......................................................................................................................... 496 Section 103.2: The Python Debugger: Step-through Debugging with _pdb_ .................................................... 496 Section 103.3: Remote debugger ............................................................................................................................. 498

Chapter 104: Reading and Writing CSV ........................................................................................................ 499 Section 104.1: Using pandas ..................................................................................................................................... 499 Section 104.2: Writing a TSV file .............................................................................................................................. 499

Chapter 105: Writing to CSV from String or List ...................................................................................... 501 Section 105.1: Basic Write Example .......................................................................................................................... 501 Section 105.2: Appending a String as a newline in a CSV file ............................................................................... 501

Chapter 106: Dynamic code execution with `exec` and `eval` ............................................................. 502 Section 106.1: Executing code provided by untrusted user using exec, eval, or ast.literal_eval ....................... 502 Section 106.2: Evaluating a string containing a Python literal with ast.literal_eval ........................................... 502 Section 106.3: Evaluating statements with exec ..................................................................................................... 502 Section 106.4: Evaluating an expression with eval ................................................................................................. 503 Section 106.5: Precompiling an expression to evaluate it multiple times ............................................................ 503 Section 106.6: Evaluating an expression with eval using custom globals ........................................................... 503

Chapter 107: PyInstaller - Distributing Python Code .............................................................................. 504 Section 107.1: Installation and Setup ........................................................................................................................ 504 Section 107.2: Using Pyinstaller ................................................................................................................................ 504 Section 107.3: Bundling to One Folder ..................................................................................................................... 505 Section 107.4: Bundling to a Single File ................................................................................................................... 505

Chapter 108: Data Visualization with Python ............................................................................................. 506 Section 108.1: Seaborn .............................................................................................................................................. 506 Section 108.2: Matplotlib ........................................................................................................................................... 508 Section 108.3: Plotly ................................................................................................................................................... 509 Section 108.4: MayaVI ............................................................................................................................................... 511

Chapter 109: The Interpreter (Command Line Console) ....................................................................... 513 Section 109.1: Getting general help .......................................................................................................................... 513 Section 109.2: Referring to the last expression ...................................................................................................... 513 Section 109.3: Opening the Python console ............................................................................................................ 514 Section 109.4: The PYTHONSTARTUP variable ...................................................................................................... 514 Section 109.5: Command line arguments ............................................................................................................... 514 Section 109.6: Getting help about an object ........................................................................................................... 515

Chapter 110: *args and **kwargs ....................................................................................................................... 518 Section 110.1: Using **kwargs when writing functions ............................................................................................ 518 Section 110.2: Using *args when writing functions .................................................................................................. 518 Section 110.3: Populating kwarg values with a dictionary ..................................................................................... 519 Section 110.4: Keyword-only and Keyword-required arguments ........................................................................ 519 Section 110.5: Using **kwargs when calling functions ............................................................................................ 519 Section 110.6: **kwargs and default values ............................................................................................................. 519 Section 110.7: Using *args when calling functions .................................................................................................. 520

Chapter 111: Garbage Collection ........................................................................................................................ 521 Section 111.1: Reuse of primitive objects .................................................................................................................. 521 Section 111.2: Eects of the del command .............................................................................................................. 521 Section 111.3: Reference Counting ............................................................................................................................ 522

Section 111.4: Garbage Collector for Reference Cycles ......................................................................................... 522 Section 111.5: Forcefully deallocating objects ......................................................................................................... 523 Section 111.6: Viewing the refcount of an object ..................................................................................................... 524 Section 111.7: Do not wait for the garbage collection to clean up ........................................................................ 524 Section 111.8: Managing garbage collection ........................................................................................................... 524

Chapter 112: Pickle data serialisation ............................................................................................................. 526 Section 112.1: Using Pickle to serialize and deserialize an object .......................................................................... 526 Section 112.2: Customize Pickled Data .................................................................................................................... 526

Chapter 113: Binary Data ...................................................................................................................................... 528 Section 113.1: Format a list of values into a byte object ........................................................................................ 528 Section 113.2: Unpack a byte object according to a format string ...................................................................... 528 Section 113.3: Packing a structure ............................................................................................................................ 528

Chapter 114: Idioms .................................................................................................................................................. 530 Section 114.1: Dictionary key initializations .............................................................................................................. 530 Section 114.2: Switching variables ............................................................................................................................ 530 Section 114.3: Use truth value testing ...................................................................................................................... 530 Section 114.4: Test for "__main__" to avoid unexpected code execution .......................................................... 531

Chapter 115: Data Serialization .......................................................................................................................... 533 Section 115.1: Serialization using JSON .................................................................................................................... 533 Section 115.2: Serialization using Pickle ................................................................................................................... 533

Chapter 116: Multiprocessing ............................................................................................................................... 535 Section 116.1: Running Two Simple Processes ......................................................................................................... 535 Section 116.2: Using Pool and Map ........................................................................................................................... 535

Chapter 117: Multithreading ................................................................................................................................. 537 Section 117.1: Basics of multithreading .................................................................................................................... 537 Section 117.2: Communicating between threads .................................................................................................... 538 Section 117.3: Creating a worker pool ...................................................................................................................... 539 Section 117.4: Advanced use of multithreads .......................................................................................................... 539 Section 117.5: Stoppable Thread with a while Loop ............................................................................................... 541

Chapter 118: Processes and Threads .............................................................................................................. 542 Section 118.1: Global Interpreter Lock ...................................................................................................................... 542 Section 118.2: Running in Multiple Threads ............................................................................................................. 543 Section 118.3: Running in Multiple Processes .......................................................................................................... 544 Section 118.4: Sharing State Between Threads ....................................................................................................... 544 Section 118.5: Sharing State Between Processes .................................................................................................... 545

Chapter 119: Python concurrency ..................................................................................................................... 546 Section 119.1: The multiprocessing module ............................................................................................................. 546 Section 119.2: The threading module ....................................................................................................................... 547 Section 119.3: Passing data between multiprocessing processes ........................................................................ 547

Chapter 120: Parallel computation .................................................................................................................. 550 Section 120.1: Using the multiprocessing module to parallelise tasks ................................................................. 550 Section 120.2: Using a C-extension to parallelize tasks ........................................................................................ 550 Section 120.3: Using Parent and Children scripts to execute code in parallel .................................................... 550 Section 120.4: Using PyPar module to parallelize .................................................................................................. 551

Chapter 121: Sockets ................................................................................................................................................ 552 Section 121.1: Raw Sockets on Linux ......................................................................................................................... 552 Section 121.2: Sending data via UDP ....................................................................................................................... 552 Section 121.3: Receiving data via UDP ..................................................................................................................... 553 Section 121.4: Sending data via TCP ........................................................................................................................ 553

Section 121.5: Multi-threaded TCP Socket Server ................................................................................................... 553

Chapter 122: Websockets ...................................................................................................................................... 556 Section 122.1: Simple Echo with aiohttp ................................................................................................................... 556 Section 122.2: Wrapper Class with aiohttp ............................................................................................................. 556 Section 122.3: Using Autobahn as a Websocket Factory ...................................................................................... 557

Chapter 123: Sockets And Message Encryption/Decryption Between Client and Server ............................................................................................................................................................................................ 559 Section 123.1: Server side Implementation .............................................................................................................. 559 Section 123.2: Client side Implementation .............................................................................................................. 561

Chapter 124: Python Networking ..................................................................................................................... 563 Section 124.1: Creating a Simple Http Server .......................................................................................................... 563 Section 124.2: Creating a TCP server ...................................................................................................................... 563 Section 124.3: Creating a UDP Server ..................................................................................................................... 564 Section 124.4: Start Simple HttpServer in a thread and open the browser ......................................................... 564 Section 124.5: The simplest Python socket client-server example ....................................................................... 565

Chapter 125: Python HTTP Server .................................................................................................................... 567 Section 125.1: Running a simple HTTP server ......................................................................................................... 567 Section 125.2: Serving files ........................................................................................................................................ 567 Section 125.3: Basic handling of GET, POST, PUT using BaseHTTPRequestHandler ......................................... 568 Section 125.4: Programmatic API of SimpleHTTPServer ....................................................................................... 569

Chapter 126: Flask .................................................................................................................................................... 571 Section 126.1: Files and Templates ........................................................................................................................... 571 Section 126.2: The basics .......................................................................................................................................... 571 Section 126.3: Routing URLs ..................................................................................................................................... 572 Section 126.4: HTTP Methods ................................................................................................................................... 573 Section 126.5: Jinja Templating ............................................................................................................................... 573 Section 126.6: The Request Object .......................................................................................................................... 574

Chapter 127: Introduction to RabbitMQ using AMQPStorm ................................................................ 576 Section 127.1: How to consume messages from RabbitMQ .................................................................................. 576 Section 127.2: How to publish messages to RabbitMQ ......................................................................................... 577 Section 127.3: How to create a delayed queue in RabbitMQ ................................................................................ 577

Chapter 128: Descriptor ......................................................................................................................................... 580 Section 128.1: Simple descriptor ............................................................................................................................... 580 Section 128.2: Two-way conversions ....................................................................................................................... 581

Chapter 129: tempfile NamedTemporaryFile ............................................................................................. 582 Section 129.1: Create (and write to a) known, persistent temporary file ............................................................. 582

Chapter 130: Input, Subset and Output External Data Files using Pandas ................................. 584 Section 130.1: Basic Code to Import, Subset and Write External Data Files Using Pandas ............................... 584

Chapter 131: Unzipping Files ................................................................................................................................ 586 Section 131.1: Using Python ZipFile.extractall() to decompress a ZIP file ............................................................ 586 Section 131.2: Using Python TarFile.extractall() to decompress a tarball ........................................................... 586

Chapter 132: Working with ZIP archives ........................................................................................................ 587 Section 132.1: Examining Zipfile Contents ............................................................................................................... 587 Section 132.2: Opening Zip Files ............................................................................................................................... 587 Section 132.3: Extracting zip file contents to a directory ....................................................................................... 588 Section 132.4: Creating new archives ...................................................................................................................... 588

Chapter 133: Getting start with GZip .............................................................................................................. 589 Section 133.1: Read and write GNU zip files ............................................................................................................ 589

Chapter 134: Stack ................................................................................................................................................... 590 Section 134.1: Creating a Stack class with a List Object ........................................................................................ 590 Section 134.2: Parsing Parentheses ......................................................................................................................... 591

Chapter 135: Working around the Global Interpreter Lock (GIL) ..................................................... 593 Section 135.1: Multiprocessing.Pool .......................................................................................................................... 593 Section 135.2: Cython nogil: ...................................................................................................................................... 594

Chapter 136: Deployment ..................................................................................................................................... 595 Section 136.1: Uploading a Conda Package ............................................................................................................ 595

Chapter 137: Logging .............................................................................................................................................. 597 Section 137.1: Introduction to Python Logging ........................................................................................................ 597 Section 137.2: Logging exceptions ........................................................................................................................... 598

Chapter 138: Web Server Gateway Interface (WSGI) ............................................................................. 601 Section 138.1: Server Object (Method) ..................................................................................................................... 601

Chapter 139: Python Server Sent Events ...................................................................................................... 602 Section 139.1: Flask SSE ............................................................................................................................................. 602 Section 139.2: Asyncio SSE ........................................................................................................................................ 602

Chapter 140: Alternatives to switch statement from other languages ....................................... 604 Section 140.1: Use what the language oers: the if/else construct ..................................................................... 604 Section 140.2: Use a dict of functions ...................................................................................................................... 604 Section 140.3: Use class introspection ..................................................................................................................... 605 Section 140.4: Using a context manager ................................................................................................................ 606

Chapter 141: List destructuring (aka packing and unpacking) ......................................................... 607 Section 141.1: Destructuring assignment ................................................................................................................. 607 Section 141.2: Packing function arguments ............................................................................................................ 608 Section 141.3: Unpacking function arguments ........................................................................................................ 610

Chapter 142: Accessing Python source code and bytecode .............................................................. 611 Section 142.1: Display the bytecode of a function .................................................................................................. 611 Section 142.2: Display the source code of an object ............................................................................................. 611 Section 142.3: Exploring the code object of a function .......................................................................................... 612

Chapter 143: Mixins .................................................................................................................................................. 613 Section 143.1: Mixin ..................................................................................................................................................... 613 Section 143.2: Overriding Methods in Mixins ........................................................................................................... 614

Chapter 144: Attribute Access ........................................................................................................................... 615 Section 144.1: Basic Attribute Access using the Dot Notation ............................................................................... 615 Section 144.2: Setters, Getters & Properties ............................................................................................................ 615

Chapter 145: ArcPy .................................................................................................................................................. 618 Section 145.1: createDissolvedGDB to create a file gdb on the workspace ........................................................ 618 Section 145.2: Printing one field's value for all rows of feature class in file geodatabase using Search Cursor ................................................................................................................................................................. 618

Chapter 146: Abstract Base Classes (abc) ................................................................................................... 619 Section 146.1: Setting the ABCMeta metaclass ....................................................................................................... 619 Section 146.2: Why/How to use ABCMeta and @abstractmethod ...................................................................... 619

Chapter 147: Plugin and Extension Classes ................................................................................................. 621 Section 147.1: Mixins ................................................................................................................................................... 621 Section 147.2: Plugins with Customized Classes ..................................................................................................... 622

Chapter 148: Immutable datatypes(int, float, str, tuple and frozensets) .................................. 624 Section 148.1: Individual characters of strings are not assignable ....................................................................... 624 Section 148.2: Tuple's individual members aren't assignable ............................................................................... 624

Section 148.3: Frozenset's are immutable and not assignable ............................................................................. 624

Chapter 149: Incompatibilities moving from Python 2 to Python 3 ................................................ 625 Section 149.1: Integer Division ................................................................................................................................... 625 Section 149.2: Unpacking Iterables .......................................................................................................................... 626 Section 149.3: Strings: Bytes versus Unicode .......................................................................................................... 628 Section 149.4: Print statement vs. Print function .................................................................................................... 630 Section 149.5: Dierences between range and xrange functions ........................................................................ 631 Section 149.6: Raising and handling Exceptions ..................................................................................................... 632 Section 149.7: Leaked variables in list comprehension .......................................................................................... 634 Section 149.8: True, False and None ........................................................................................................................ 635 Section 149.9: User Input ........................................................................................................................................... 635 Section 149.10: Comparison of dierent types ....................................................................................................... 636 Section 149.11: .next() method on iterators renamed ............................................................................................. 636 Section 149.12: filter(), map() and zip() return iterators instead of sequences ................................................... 637 Section 149.13: Renamed modules ........................................................................................................................... 637 Section 149.14: Removed operators and ``, synonymous with != and repr() ................................................... 638 Section 149.15: long vs. int ......................................................................................................................................... 638 Section 149.16: All classes are "new-style classes" in Python 3 ............................................................................ 639 Section 149.17: Reduce is no longer a built-in ......................................................................................................... 640 Section 149.18: Absolute/Relative Imports .............................................................................................................. 640 Section 149.19: map() ................................................................................................................................................ 642 Section 149.20: The round() function tie-breaking and return type .................................................................... 643 Section 149.21: File I/O .............................................................................................................................................. 644 Section 149.22: cmp function removed in Python 3 ............................................................................................... 644 Section 149.23: Octal Constants ............................................................................................................................... 645 Section 149.24: Return value when writing to a file object .................................................................................... 645 Section 149.25: exec statement is a function in Python 3 ...................................................................................... 645 Section 149.26: encode/decode to hex no longer available ................................................................................. 646 Section 149.27: Dictionary method changes .......................................................................................................... 647 Section 149.28: Class Boolean Value ....................................................................................................................... 647 Section 149.29: hasattr function bug in Python 2 ................................................................................................... 648

Chapter 150: 2to3 tool ............................................................................................................................................ 650 Section 150.1: Basic Usage ........................................................................................................................................ 650

Chapter 151: Non-ocial Python implementations ................................................................................ 652 Section 151.1: IronPython ........................................................................................................................................... 652 Section 151.2: Jython ................................................................................................................................................. 652 Section 151.3: Transcrypt .......................................................................................................................................... 653

Chapter 152: Abstract syntax tree ................................................................................................................... 656 Section 152.1: Analyze functions in a python script ................................................................................................ 656

Chapter 153: Unicode and bytes ....................................................................................................................... 658 Section 153.1: Encoding/decoding error handling .................................................................................................. 658 Section 153.2: File I/O ................................................................................................................................................ 658 Section 153.3: Basics .................................................................................................................................................. 659

Chapter 154: Python Serial Communication (pyserial) ......................................................................... 661 Section 154.1: Initialize serial device ......................................................................................................................... 661 Section 154.2: Read from serial port ....................................................................................................................... 661 Section 154.3: Check what serial ports are available on your machine .............................................................. 661

Chapter 155: Neo4j and Cypher using Py2Neo ......................................................................................... 664 Section 155.1: Adding Nodes to Neo4j Graph .......................................................................................................... 664

Section 155.2: Importing and Authenticating .......................................................................................................... 664 Section 155.3: Adding Relationships to Neo4j Graph ............................................................................................. 664 Section 155.4: Query 1 : Autocomplete on News Titles .......................................................................................... 664 Section 155.5: Query 2 : Get News Articles by Location on a particular date ..................................................... 665 Section 155.6: Cypher Query Samples .................................................................................................................... 665

Chapter 156: Basic Curses with Python .......................................................................................................... 666 Section 156.1: The wrapper() helper function ......................................................................................................... 666 Section 156.2: Basic Invocation Example ................................................................................................................ 666

Chapter 157: Templates in python ................................................................................................................... 667 Section 157.1: Simple data output program using template ................................................................................. 667 Section 157.2: Changing delimiter ............................................................................................................................ 667

Chapter 158: Pillow ................................................................................................................................................... 668 Section 158.1: Read Image File ................................................................................................................................. 668 Section 158.2: Convert files to JPEG ........................................................................................................................ 668

Chapter 159: The pass statement .................................................................................................................... 669 Section 159.1: Ignore an exception ........................................................................................................................... 669 Section 159.2: Create a new Exception that can be caught .................................................................................. 669

Chapter 160: CLI subcommands with precise help output .................................................................. 671 Section 160.1: Native way (no libraries) ................................................................................................................... 671 Section 160.2: argparse (default help formatter) .................................................................................................. 671 Section 160.3: argparse (custom help formatter) .................................................................................................. 672

Chapter 161: Database Access ............................................................................................................................ 674 Section 161.1: SQLite ................................................................................................................................................... 674 Section 161.2: Accessing MySQL database using MySQLdb ................................................................................. 679 Section 161.3: Connection .......................................................................................................................................... 680 Section 161.4: PostgreSQL Database access using psycopg2 .............................................................................. 681 Section 161.5: Oracle database ................................................................................................................................ 682 Section 161.6: Using sqlalchemy ............................................................................................................................... 684

Chapter 162: Connecting Python to SQL Server ....................................................................................... 685 Section 162.1: Connect to Server, Create Table, Query Data ................................................................................ 685

Chapter 163: PostgreSQL ...................................................................................................................................... 686 Section 163.1: Getting Started ................................................................................................................................... 686

Chapter 164: Python and Excel .......................................................................................................................... 687 Section 164.1: Read the excel data using xlrd module ........................................................................................... 687 Section 164.2: Format Excel files with xlsxwriter ..................................................................................................... 687 Section 164.3: Put list data into a Excel's file ........................................................................................................... 688 Section 164.4: OpenPyXL .......................................................................................................................................... 689 Section 164.5: Create excel charts with xlsxwriter .................................................................................................. 689

Chapter 165: Turtle Graphics .............................................................................................................................. 693 Section 165.1: Ninja Twist (Turtle Graphics) ............................................................................................................ 693

Chapter 166: Python Persistence ...................................................................................................................... 694 Section 166.1: Python Persistence ............................................................................................................................ 694 Section 166.2: Function utility for save and load .................................................................................................... 695

Chapter 167: Design Patterns ............................................................................................................................. 696 Section 167.1: Introduction to design patterns and Singleton Pattern ................................................................. 696 Section 167.2: Strategy Pattern ................................................................................................................................ 698 Section 167.3: Proxy ................................................................................................................................................... 699

Chapter 168: hashlib ................................................................................................................................................ 701

Section 168.1: MD5 hash of a string ......................................................................................................................... 701 Section 168.2: algorithm provided by OpenSSL ..................................................................................................... 702

Chapter 169: Creating a Windows service using Python ....................................................................... 703 Section 169.1: A Python script that can be run as a service .................................................................................. 703 Section 169.2: Running a Flask web application as a service ............................................................................... 704

Chapter 170: Mutable vs Immutable (and Hashable) in Python ....................................................... 706 Section 170.1: Mutable vs Immutable ....................................................................................................................... 706 Section 170.2: Mutable and Immutable as Arguments .......................................................................................... 708

Chapter 171: configparser .................................................................................................................................... 710 Section 171.1: Creating configuration file programmatically ................................................................................. 710 Section 171.2: Basic usage ......................................................................................................................................... 710

Chapter 172: Optical Character Recognition .............................................................................................. 711 Section 172.1: PyTesseract ........................................................................................................................................ 711 Section 172.2: PyOCR ................................................................................................................................................ 711

Chapter 173: Virtual environments .................................................................................................................. 713 Section 173.1: Creating and using a virtual environment ....................................................................................... 713 Section 173.2: Specifying specific python version to use in script on Unix/Linux ............................................... 715 Section 173.3: Creating a virtual environment for a dierent version of python ............................................... 715 Section 173.4: Making virtual environments using Anaconda .............................................................................. 715 Section 173.5: Managing multiple virtual environments with virtualenvwrapper ............................................... 716 Section 173.6: Installing packages in a virtual environment ................................................................................. 717 Section 173.7: Discovering which virtual environment you are using .................................................................. 718 Section 173.8: Checking if running inside a virtual environment .......................................................................... 719 Section 173.9: Using virtualenv with fish shell ......................................................................................................... 719

Chapter 174: Python Virtual Environment - virtualenv ......................................................................... 721 Section 174.1: Installation .......................................................................................................................................... 721 Section 174.2: Usage ................................................................................................................................................. 721 Section 174.3: Install a package in your Virtualenv ............................................................................................... 721 Section 174.4: Other useful virtualenv commands ................................................................................................. 722

Chapter 175: Virtual environment with virtualenvwrapper ................................................................ 724 Section 175.1: Create virtual environment with virtualenvwrapper ...................................................................... 724

Chapter 176: Create virtual environment with virtualenvwrapper in windows ........................ 726 Section 176.1: Virtual environment with virtualenvwrapper for windows ............................................................. 726

Chapter 177: sys ........................................................................................................................................................ 727 Section 177.1: Command line arguments ................................................................................................................ 727 Section 177.2: Script name ........................................................................................................................................ 727 Section 177.3: Standard error stream ...................................................................................................................... 727 Section 177.4: Ending the process prematurely and returning an exit code ....................................................... 727

Chapter 178: ChemPy - python package ...................................................................................................... 728 Section 178.1: Parsing formulae ............................................................................................................................... 728 Section 178.2: Balancing stoichiometry of a chemical reaction ........................................................................... 728 Section 178.3: Balancing reactions .......................................................................................................................... 728 Section 178.4: Chemical equilibria ............................................................................................................................ 729 Section 178.5: Ionic strength ..................................................................................................................................... 729 Section 178.6: Chemical kinetics (system of ordinary dierential equations) .................................................... 729

Chapter 179: pygame .............................................................................................................................................. 731 Section 179.1: Pygame's mixer module .................................................................................................................... 731 Section 179.2: Installing pygame .............................................................................................................................. 732

Chapter 180: Pyglet ................................................................................................................................................. 734 Section 180.1: Installation of Pyglet .......................................................................................................................... 734 Section 180.2: Hello World in Pyglet ........................................................................................................................ 734 Section 180.3: Playing Sound in Pyglet .................................................................................................................... 734 Section 180.4: Using Pyglet for OpenGL ................................................................................................................. 734 Section 180.5: Drawing Points Using Pyglet and OpenGL ..................................................................................... 734

Chapter 181: Audio .................................................................................................................................................... 736 Section 181.1: Working with WAV files ...................................................................................................................... 736 Section 181.2: Convert any soundfile with python and mpeg ............................................................................ 736 Section 181.3: Playing Windows' beeps .................................................................................................................... 736 Section 181.4: Audio With Pyglet .............................................................................................................................. 737

Chapter 182: pyaudio .............................................................................................................................................. 738 Section 182.1: Callback Mode Audio I/O .................................................................................................................. 738 Section 182.2: Blocking Mode Audio I/O ................................................................................................................. 739

Chapter 183: shelve .................................................................................................................................................. 741 Section 183.1: Creating a new Shelf .......................................................................................................................... 741 Section 183.2: Sample code for shelve .................................................................................................................... 742 Section 183.3: To summarize the interface (key is a string, data is an arbitrary object): .................................. 742 Section 183.4: Write-back ......................................................................................................................................... 742

Chapter 184: IoT Programming with Python and Raspberry PI ....................................................... 744 Section 184.1: Example - Temperature sensor ........................................................................................................ 744

Chapter 185: kivy - Cross-platform Python Framework for NUI Development ....................... 748 Section 185.1: First App .............................................................................................................................................. 748

Chapter 186: Pandas Transform: Preform operations on groups and concatenate the results ............................................................................................................................................................................. 750 Section 186.1: Simple transform ............................................................................................................................... 750 Section 186.2: Multiple results per group ................................................................................................................ 751

Chapter 187: Similarities in syntax, Dierences in meaning: Python vs. JavaScript ............. 752 Section 187.1: `in` with lists ......................................................................................................................................... 752

Chapter 188: Call Python from C# ................................................................................................................... 753 Section 188.1: Python script to be called by C# application .................................................................................. 753 Section 188.2: C# code calling Python script .......................................................................................................... 753

Chapter 189: ctypes ................................................................................................................................................. 755 Section 189.1: ctypes arrays ..................................................................................................................................... 755 Section 189.2: Wrapping functions for ctypes ........................................................................................................ 755 Section 189.3: Basic usage ........................................................................................................................................ 756 Section 189.4: Common pitfalls ................................................................................................................................ 756 Section 189.5: Basic ctypes object ........................................................................................................................... 757 Section 189.6: Complex usage .................................................................................................................................. 758

Chapter 190: Writing extensions ........................................................................................................................ 760 Section 190.1: Hello World with C Extension ............................................................................................................ 760 Section 190.2: C Extension Using c++ and Boost .................................................................................................... 760 Section 190.3: Passing an open file to C Extensions .............................................................................................. 762

Chapter 191: Python Lex-Yacc ............................................................................................................................. 763 Section 191.1: Getting Started with PLY .................................................................................................................... 763 Section 191.2: The "Hello, World!" of PLY - A Simple Calculator ............................................................................ 763 Section 191.3: Part 1: Tokenizing Input with Lex ....................................................................................................... 765 Section 191.4: Part 2: Parsing Tokenized Input with Yacc ...................................................................................... 768

Chapter 192: Unit Testing ...................................................................................................................................... 772 Section 192.1: Test Setup and Teardown within a unittest.TestCase .................................................................... 772 Section 192.2: Asserting on Exceptions ................................................................................................................... 772 Section 192.3: Testing Exceptions ............................................................................................................................ 773 Section 192.4: Choosing Assertions Within Unittests ............................................................................................. 774 Section 192.5: Unit tests with pytest ........................................................................................................................ 775 Section 192.6: Mocking functions with unittest.mock.create_autospec ............................................................... 778

Chapter 193: py.test ................................................................................................................................................. 780 Section 193.1: Setting up py.test ............................................................................................................................... 780 Section 193.2: Intro to Test Fixtures ......................................................................................................................... 780 Section 193.3: Failing Tests ....................................................................................................................................... 783

Chapter 194: Profiling ............................................................................................................................................. 785 Section 194.1: %%timeit and %timeit in IPython ...................................................................................................... 785 Section 194.2: Using cProfile (Preferred Profiler) ................................................................................................... 785 Section 194.3: timeit() function ................................................................................................................................. 785 Section 194.4: timeit command line ......................................................................................................................... 786 Section 194.5: line_profiler in command line .......................................................................................................... 786

Chapter 195: Python speed of program ....................................................................................................... 788 Section 195.1: Deque operations .............................................................................................................................. 788 Section 195.2: Algorithmic Notations ....................................................................................................................... 788 Section 195.3: Notation .............................................................................................................................................. 789 Section 195.4: List operations ................................................................................................................................... 790 Section 195.5: Set operations ................................................................................................................................... 790

Chapter 196: Performance optimization ....................................................................................................... 792 Section 196.1: Code profiling ..................................................................................................................................... 792

Chapter 197: Security and Cryptography .................................................................................................... 794 Section 197.1: Secure Password Hashing ................................................................................................................. 794 Section 197.2: Calculating a Message Digest ......................................................................................................... 794 Section 197.3: Available Hashing Algorithms .......................................................................................................... 794 Section 197.4: File Hashing ....................................................................................................................................... 795 Section 197.5: Generating RSA signatures using pycrypto ................................................................................... 795 Section 197.6: Asymmetric RSA encryption using pycrypto ................................................................................. 796 Section 197.7: Symmetric encryption using pycrypto ............................................................................................ 797

Chapter 198: Secure Shell Connection in Python ...................................................................................... 798 Section 198.1: ssh connection ................................................................................................................................... 798

Chapter 199: Python Anti-Patterns .................................................................................................................. 799 Section 199.1: Overzealous except clause ............................................................................................................... 799 Section 199.2: Looking before you leap with processor-intensive function ........................................................ 799

Chapter 200: Common Pitfalls ........................................................................................................................... 802 Section 200.1: List multiplication and common references .................................................................................. 802 Section 200.2: Mutable default argument .............................................................................................................. 805 Section 200.3: Changing the sequence you are iterating over ............................................................................ 806 Section 200.4: Integer and String identity .............................................................................................................. 809 Section 200.5: Dictionaries are unordered ............................................................................................................. 810 Section 200.6: Variable leaking in list comprehensions and for loops ................................................................ 811 Section 200.7: Chaining of or operator ................................................................................................................... 811 Section 200.8: sys.argv[0] is the name of the file being executed ...................................................................... 812 Section 200.9: Accessing int literals' attributes ...................................................................................................... 812 Section 200.10: Global Interpreter Lock (GIL) and blocking threads ................................................................... 813

Section 200.11: Multiple return .................................................................................................................................. 814 Section 200.12: Pythonic JSON keys ....................................................................................................................... 814

Chapter 201: Hidden Features ............................................................................................................................ 816 Section 201.1: Operator Overloading ....................................................................................................................... 816

Credits ............................................................................................................................................................................ 817 You may also like ...................................................................................................................................................... 831

About

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This Python® Notes for Professionals book is compiled from Stack Overflow Documentation, the content is written by the beautiful people at Stack Overflow. Text content is released under Creative Commons BY-SA, see credits at the end of this book whom contributed to the various chapters. Images may be copyright of their respective owners unless otherwise specified This is an unofficial free book created for educational purposes and is not affiliated with official Python® group(s) or company(s) nor Stack Overflow. All trademarks and registered trademarks are the property of their respective company owners The information presented in this book is not guaranteed to be correct nor accurate, use at your own risk Please send feedback and corrections to [email protected]

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Chapter 1: Getting started with Python Language Python 3.x Version Release Date 3.8 2020-04-29 3.7

2018-06-27

3.6

2016-12-23

3.5

2015-09-13

3.4

2014-03-17

3.3

2012-09-29

3.2

2011-02-20

3.1

2009-06-26

3.0

2008-12-03

Python 2.x Version Release Date 2.7 2010-07-03 2.6

2008-10-02

2.5

2006-09-19

2.4

2004-11-30

2.3

2003-07-29

2.2

2001-12-21

2.1

2001-04-15

2.0

2000-10-16

Section 1.1: Getting Started Python is a widely used high-level programming language for general-purpose programming, created by Guido van Rossum and first released in 1991. Python features a dynamic type system and automatic memory management and supports multiple programming paradigms, including object-oriented, imperative, functional programming, and procedural styles. It has a large and comprehensive standard library. Two major versions of Python are currently in active use: Python 3.x is the current version and is under active development. Python 2.x is the legacy version and will receive only security updates until 2020. No new features will be implemented. Note that many projects still use Python 2, although migrating to Python 3 is getting easier. You can download and install either version of Python here. See Python 3 vs. Python 2 for a comparison between them. In addition, some third-parties offer re-packaged versions of Python that add commonly used libraries and other features to ease setup for common use cases, such as math, data analysis or scientific use. See the list at the official site. Verify if Python is installed To confirm that Python was installed correctly, you can verify that by running the following command in your favorite terminal (If you are using Windows OS, you need to add path of python to the environment variable before using it in command prompt):

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$ python --version

Python 3.x Version

≥ 3.0

If you have Python 3 installed, and it is your default version (see Troubleshooting for more details) you should see something like this: $ python --version Python 3.6.0

Python 2.x Version

≤ 2.7

If you have Python 2 installed, and it is your default version (see Troubleshooting for more details) you should see something like this: $ python --version Python 2.7.13

If you have installed Python 3, but $ python --version outputs a Python 2 version, you also have Python 2 installed. This is often the case on MacOS, and many Linux distributions. Use $ python3 instead to explicitly use the Python 3 interpreter. Hello, World in Python using IDLE IDLE is a simple editor for Python, that comes bundled with Python. How to create Hello, World program in IDLE Open IDLE on your system of choice. In older versions of Windows, it can be found at All Programs under the Windows menu. In Windows 8+, search for IDLE or find it in the apps that are present in your system. On Unix-based (including Mac) systems you can open it from the shell by typing $ idle python_file.py.

It will open a shell with options along the top. In the shell, there is a prompt of three right angle brackets: >>>

Now write the following code in the prompt: >>> print("Hello, World")

Hit Enter . >>> print("Hello, World") Hello, World

Hello World Python file Create a new file hello.py that contains the following line: Python 3.x Version

≥ 3.0

print('Hello, World')

Python 2.x Version

≥ 2.6

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You can use the Python 3 print function in Python 2 with the following import statement: from __future__ import print_function

Python 2 has a number of functionalities that can be optionally imported from Python 3 using the __future__ module, as discussed here. Python 2.x Version

≤ 2.7

If using Python 2, you may also type the line below. Note that this is not valid in Python 3 and thus not recommended because it reduces cross-version code compatibility. print 'Hello, World'

In your terminal, navigate to the directory containing the file hello.py. Type python hello.py, then hit the Enter key. $ python hello.py Hello, World

You should see Hello, World printed to the console. You can also substitute hello.py with the path to your file. For example, if you have the file in your home directory and your user is "user" on Linux, you can type python /home/user/hello.py. Launch an interactive Python shell By executing (running) the python command in your terminal, you are presented with an interactive Python shell. This is also known as the Python Interpreter or a REPL (for 'Read Evaluate Print Loop'). $ python Python 2.7.12 (default, Jun 28 2016, 08:46:01) [GCC 6.1.1 20160602] on linux Type "help", "copyright", "credits" or "license" for more information. >>> print 'Hello, World' Hello, World >>>

If you want to run Python 3 from your terminal, execute the command python3. $ python3 Python 3.6.0 (default, Jan 13 2017, 00:00:00) [GCC 6.1.1 20160602] on linux Type "help", "copyright", "credits" or "license" for more information. >>> print('Hello, World') Hello, World >>>

Alternatively, start the interactive prompt and load file with python -i . In command line, run: $ python -i hello.py "Hello World" >>>

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There are multiple ways to close the Python shell: >>> exit()

or >>> quit()

Alternatively, CTRL + D will close the shell and put you back on your terminal's command line. If you want to cancel a command you're in the middle of typing and get back to a clean command prompt, while staying inside the Interpreter shell, use CTRL + C . Try an interactive Python shell online. Other Online Shells Various websites provide online access to Python shells. Online shells may be useful for the following purposes: Run a small code snippet from a machine which lacks python installation(smartphones, tablets etc). Learn or teach basic Python. Solve online judge problems. Examples: Disclaimer: documentation author(s) are not affiliated with any resources listed below. https://www.python.org/shell/ - The online Python shell hosted by the official Python website. https://ideone.com/ - Widely used on the Net to illustrate code snippet behavior. https://repl.it/languages/python3 - Powerful and simple online compiler, IDE and interpreter. Code, compile, and run code in Python. https://www.tutorialspoint.com/execute_python_online.php - Full-featured UNIX shell, and a user-friendly project explorer. http://rextester.com/l/python3_online_compiler - Simple and easy to use IDE which shows execution time Run commands as a string Python can be passed arbitrary code as a string in the shell: $ python -c 'print("Hello, World")' Hello, World

This can be useful when concatenating the results of scripts together in the shell. Shells and Beyond Package Management - The PyPA recommended tool for installing Python packages is PIP. To install, on your command line execute pip install . For instance, pip install numpy. (Note: On windows you must add pip to your PATH environment variables. To avoid this, use python -m pip install )

Shells - So far, we have discussed different ways to run code using Python's native interactive shell. Shells use GoalKicker.com – Python® Notes for Professionals

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Python's interpretive power for experimenting with code real-time. Alternative shells include IDLE - a pre-bundled GUI, IPython - known for extending the interactive experience, etc. Programs - For long-term storage you can save content to .py files and edit/execute them as scripts or programs with external tools e.g. shell, IDEs (such as PyCharm), Jupyter notebooks, etc. Intermediate users may use these tools; however, the methods discussed here are sufficient for getting started. Python tutor allows you to step through Python code so you can visualize how the program will flow, and helps you to understand where your program went wrong. PEP8 defines guidelines for formatting Python code. Formatting code well is important so you can quickly read what the code does.

Section 1.2: Creating variables and assigning values To create a variable in Python, all you need to do is specify the variable name, and then assign a value to it. =

Python uses = to assign values to variables. There's no need to declare a variable in advance (or to assign a data type to it), assigning a value to a variable itself declares and initializes the variable with that value. There's no way to declare a variable without assigning it an initial value. # Integer a = 2 print(a) # Output: 2 # Integer b = 9223372036854775807 print(b) # Output: 9223372036854775807 # Floating point pi = 3.14 print(pi) # Output: 3.14 # String c = 'A' print(c) # Output: A # String name = 'John Doe' print(name) # Output: John Doe # Boolean q = True print(q) # Output: True # Empty value or null data type x = None print(x) # Output: None

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Variable assignment works from left to right. So the following will give you an syntax error. 0 = x => Output: SyntaxError: can't assign to literal

You can not use python's keywords as a valid variable name. You can see the list of keyword by: import keyword print(keyword.kwlist)

Rules for variable naming: 1. Variables names must start with a letter or an underscore. x = True _y = True

# valid # valid

9x = False # starts with numeral => SyntaxError: invalid syntax $y = False # starts with symbol => SyntaxError: invalid syntax

2. The remainder of your variable name may consist of letters, numbers and underscores. has_0_in_it = "Still Valid"

3. Names are case sensitive. x = 9 y = X*5 =>NameError: name 'X' is not defined

Even though there's no need to specify a data type when declaring a variable in Python, while allocating the necessary area in memory for the variable, the Python interpreter automatically picks the most suitable built-in type for it: a = 2 print(type(a)) # Output: b = 9223372036854775807 print(type(b)) # Output: pi = 3.14 print(type(pi)) # Output: c = 'A' print(type(c)) # Output: name = 'John Doe' print(type(name)) # Output: q = True print(type(q))

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# Output: x = None print(type(x)) # Output:

Now you know the basics of assignment, let's get this subtlety about assignment in python out of the way. When you use = to do an assignment operation, what's on the left of = is a name for the object on the right. Finally, what = does is assign the reference of the object on the right to the name on the left. That is: a_name = an_object

# "a_name" is now a name for the reference to the object "an_object"

So, from many assignment examples above, if we pick pi = 3.14, then pi is a name (not the name, since an object can have multiple names) for the object 3.14. If you don't understand something below, come back to this point and read this again! Also, you can take a look at this for a better understanding. You can assign multiple values to multiple variables in one line. Note that there must be the same number of arguments on the right and left sides of the = operator: a, b, c = 1, 2, 3 print(a, b, c) # Output: 1 2 3 a, b, c = 1, 2 => Traceback (most recent call last): => File "name.py", line N, in => a, b, c = 1, 2 => ValueError: need more than 2 values to unpack a, b = 1, 2, 3 => Traceback (most recent call last): => File "name.py", line N, in => a, b = 1, 2, 3 => ValueError: too many values to unpack

The error in last example can be obviated by assigning remaining values to equal number of arbitrary variables. This dummy variable can have any name, but it is conventional to use the underscore (_) for assigning unwanted values: a, b, _ = 1, 2, 3 print(a, b) # Output: 1, 2

Note that the number of _ and number of remaining values must be equal. Otherwise 'too many values to unpack error' is thrown as above: a, b, _ = 1,2,3,4 =>Traceback (most recent call last): =>File "name.py", line N, in =>a, b, _ = 1,2,3,4 =>ValueError: too many values to unpack (expected 3)

You can also assign a single value to several variables simultaneously.

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a = b = c = 1 print(a, b, c) # Output: 1 1 1

When using such cascading assignment, it is important to note that all three variables a, b and c refer to the same object in memory, an int object with the value of 1. In other words, a, b and c are three different names given to the same int object. Assigning a different object to one of them afterwards doesn't change the others, just as expected: a = b = c = print(a, b, # Output: 1 b = 2 print(a, b, # Output: 1

1 c) 1 1

# all three names a, b and c refer to same int object with value 1

# b now refers to another int object, one with a value of 2 c) 2 1

# so output is as expected.

The above is also true for mutable types (like list, dict, etc.) just as it is true for immutable types (like int, string, tuple, etc.): x = y = [7, 8, 9] # x and y refer to the same list object just created, [7, 8, 9] x = [13, 8, 9] # x now refers to a different list object just created, [13, 8, 9] print(y) # y still refers to the list it was first assigned # Output: [7, 8, 9]

So far so good. Things are a bit different when it comes to modifying the object (in contrast to assigning the name to a different object, which we did above) when the cascading assignment is used for mutable types. Take a look below, and you will see it first hand: x = y = [7, 8, 9] 8, 9] x[0] = 13 in this case print(y) # Output: [13, 8, 9]

# x and y are two different names for the same list object just created, [7, # we are updating the value of the list [7, 8, 9] through one of its names, x # printing the value of the list using its other name # hence, naturally the change is reflected

Nested lists are also valid in python. This means that a list can contain another list as an element. x = [1, 2, [3, 4, 5], 6, 7] # this is nested list print x[2] # Output: [3, 4, 5] print x[2][1] # Output: 4

Lastly, variables in Python do not have to stay the same type as which they were first defined -- you can simply use = to assign a new value to a variable, even if that value is of a different type. a = 2 print(a) # Output: 2 a = "New value" print(a) # Output: New value

If this bothers you, think about the fact that what's on the left of = is just a name for an object. First you call the int object with value 2 a, then you change your mind and decide to give the name a to a string object, having value 'New value'. Simple, right? GoalKicker.com – Python® Notes for Professionals

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Section 1.3: Block Indentation Python uses indentation to define control and loop constructs. This contributes to Python's readability, however, it requires the programmer to pay close attention to the use of whitespace. Thus, editor miscalibration could result in code that behaves in unexpected ways. Python uses the colon symbol (:) and indentation for showing where blocks of code begin and end (If you come from another language, do not confuse this with somehow being related to the ternary operator). That is, blocks in Python, such as functions, loops, if clauses and other constructs, have no ending identifiers. All blocks start with a colon and then contain the indented lines below it. For example: def my_function(): a = 2 return a print(my_function())

# # # #

This This This This

is a line line line

function definition. Note the colon (:) belongs to the function because it's indented also belongs to the same function is OUTSIDE the function block

# # # #

If block starts here This is part of the if block else must be at the same level as if This line is part of the else block

or if a > b: print(a) else: print(b)

Blocks that contain exactly one single-line statement may be put on the same line, though this form is generally not considered good style: if a > b: print(a) else: print(b)

Attempting to do this with more than a single statement will not work: if x > y: y = x print(y) # IndentationError: unexpected indent if x > y: while y != z: y -= 1

# SyntaxError: invalid syntax

An empty block causes an IndentationError. Use pass (a command that does nothing) when you have a block with no content: def will_be_implemented_later(): pass

Spaces vs. Tabs In short: always use 4 spaces for indentation. Using tabs exclusively is possible but PEP 8, the style guide for Python code, states that spaces are preferred. Python 3.x Version

≥ 3.0

Python 3 disallows mixing the use of tabs and spaces for indentation. In such case a compile-time error is generated: Inconsistent use of tabs and spaces in indentation and the program will not run. Python 2.x Version

≤ 2.7

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Python 2 allows mixing tabs and spaces in indentation; this is strongly discouraged. The tab character completes the previous indentation to be a multiple of 8 spaces. Since it is common that editors are configured to show tabs as multiple of 4 spaces, this can cause subtle bugs. Citing PEP 8: When invoking the Python 2 command line interpreter with the -t option, it issues warnings about code that illegally mixes tabs and spaces. When using -tt these warnings become errors. These options are highly recommended!

Many editors have "tabs to spaces" configuration. When configuring the editor, one should differentiate between the tab character ('\t') and the Tab key. The tab character should be configured to show 8 spaces, to match the language semantics - at least in cases when (accidental) mixed indentation is possible. Editors can also automatically convert the tab character to spaces. However, it might be helpful to configure the editor so that pressing the Tab key will insert 4 spaces, instead of inserting a tab character. Python source code written with a mix of tabs and spaces, or with non-standard number of indentation spaces can be made pep8-conformant using autopep8. (A less powerful alternative comes with most Python installations: reindent.py)

Section 1.4: Datatypes Built-in Types Booleans bool: A boolean value of either True or False. Logical operations like and, or, not can be performed on booleans. x or y x and y not x

# if x is False then y otherwise x # if x is False then x otherwise y # if x is True then False, otherwise True

In Python 2.x and in Python 3.x, a boolean is also an int. The bool type is a subclass of the int type and True and False are its only instances: issubclass(bool, int) # True isinstance(True, bool) # True isinstance(False, bool) # True

If boolean values are used in arithmetic operations, their integer values (1 and 0 for True and False) will be used to return an integer result: True + False == 1 # 1 + 0 == 1 True * True == 1 # 1 * 1 == 1

Numbers int: Integer number

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a b c d

= = = =

2 100 123456789 38563846326424324

Integers in Python are of arbitrary sizes. Note: in older versions of Python, a long type was available and this was distinct from int. The two have been unified. float: Floating point number; precision depends on the implementation and system architecture, for

CPython the float datatype corresponds to a C double. a = 2.0 b = 100.e0 c = 123456789.e1 complex: Complex numbers a = 2 + 1j b = 100 + 10j

The = operators will raise a TypeError exception when any operand is a complex number. Strings Python 3.x Version

≥ 3.0

str: a unicode string. The type of 'hello' bytes: a byte string. The type of b'hello'

Python 2.x Version

≤ 2.7

str: a byte string. The type of 'hello' bytes: synonym for str unicode: a unicode string. The type of u'hello'

Sequences and collections Python differentiates between ordered sequences and unordered collections (such as set and dict). strings (str, bytes, unicode) are sequences reversed: A reversed order of str with reversed function a = reversed('hello') tuple: An ordered collection of n values of any type (n >= 0). a = (1, 2, 3) b = ('a', 1, 'python', (1, 2)) b[2] = 'something else' # returns a TypeError

Supports indexing; immutable; hashable if all its members are hashable

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list: An ordered collection of n values (n >= 0) a = [1, 2, 3] b = ['a', 1, 'python', (1, 2), [1, 2]] b[2] = 'something else' # allowed

Not hashable; mutable. set: An unordered collection of unique values. Items must be hashable. a = {1, 2, 'a'} dict: An unordered collection of unique key-value pairs; keys must be hashable. a = {1: 'one', 2: 'two'} b = {'a': [1, 2, 3], 'b': 'a string'}

An object is hashable if it has a hash value which never changes during its lifetime (it needs a __hash__() method), and can be compared to other objects (it needs an __eq__() method). Hashable objects which compare equality must have the same hash value. Built-in constants In conjunction with the built-in datatypes there are a small number of built-in constants in the built-in namespace: True: The true value of the built-in type bool False: The false value of the built-in type bool None: A singleton object used to signal that a value is absent. Ellipsis or ...: used in core Python3+ anywhere and limited usage in Python2.7+ as part of array notation. numpy and related packages use this as a 'include everything' reference in arrays. NotImplemented: a singleton used to indicate to Python that a special method doesn't support the specific

arguments, and Python will try alternatives if available. a = None # No value will be assigned. Any valid datatype can be assigned later

Python 3.x Version

≥ 3.0

None doesn't have any natural ordering. Using ordering comparison operators () isn't supported anymore

and will raise a TypeError. Python 2.x Version

≤ 2.7

None is always less than any number (None < -32 evaluates to True).

Testing the type of variables In python, we can check the datatype of an object using the built-in function type. a = '123' print(type(a))

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# Out: b = 123 print(type(b)) # Out:

In conditional statements it is possible to test the datatype with isinstance. However, it is usually not encouraged to rely on the type of the variable. i = 7 if isinstance(i, int): i += 1 elif isinstance(i, str): i = int(i) i += 1

For information on the differences between type() and isinstance() read: Differences between isinstance and type in Python To test if something is of NoneType: x = None if x is None: print('Not a surprise, I just defined x as None.')

Converting between datatypes You can perform explicit datatype conversion. For example, '123' is of str type and it can be converted to integer using int function. a = '123' b = int(a)

Converting from a float string such as '123.456' can be done using float function. a b c d

= = = =

'123.456' float(a) int(a) # ValueError: invalid literal for int() with base 10: '123.456' int(b) # 123

You can also convert sequence or collection types a = 'hello' list(a) # ['h', 'e', 'l', 'l', 'o'] set(a) # {'o', 'e', 'l', 'h'} tuple(a) # ('h', 'e', 'l', 'l', 'o')

Explicit string type at definition of literals With one letter labels just in front of the quotes you can tell what type of string you want to define. b'foo bar': results bytes in Python 3, str in Python 2 u'foo bar': results str in Python 3, unicode in Python 2 'foo bar': results str r'foo bar': results so called raw string, where escaping special characters is not necessary, everything is

taken verbatim as you typed normal

= 'foo\nbar'

# foo

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escaped = 'foo\\nbar' raw = r'foo\nbar'

# bar # foo\nbar # foo\nbar

Mutable and Immutable Data Types An object is called mutable if it can be changed. For example, when you pass a list to some function, the list can be changed: def f(m): m.append(3) x = [1, 2] f(x) x == [1, 2]

# adds a number to the list. This is a mutation.

# False now, since an item was added to the list

An object is called immutable if it cannot be changed in any way. For example, integers are immutable, since there's no way to change them: def bar(): x = (1, 2) g(x) x == (1, 2)

# Will always be True, since no function can change the object (1, 2)

Note that variables themselves are mutable, so we can reassign the variable x, but this does not change the object that x had previously pointed to. It only made x point to a new object. Data types whose instances are mutable are called mutable data types, and similarly for immutable objects and datatypes. Examples of immutable Data Types: int, long, float, complex str bytes tuple frozenset

Examples of mutable Data Types: bytearray list set dict

Section 1.5: Collection Types There are a number of collection types in Python. While types such as int and str hold a single value, collection types hold multiple values. Lists The list type is probably the most commonly used collection type in Python. Despite its name, a list is more like an array in other languages, mostly JavaScript. In Python, a list is merely an ordered collection of valid Python values. A list can be created by enclosing values, separated by commas, in square brackets:

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int_list = [1, 2, 3] string_list = ['abc', 'defghi']

A list can be empty: empty_list = []

The elements of a list are not restricted to a single data type, which makes sense given that Python is a dynamic language: mixed_list = [1, 'abc', True, 2.34, None]

A list can contain another list as its element: nested_list = [['a', 'b', 'c'], [1, 2, 3]]

The elements of a list can be accessed via an index, or numeric representation of their position. Lists in Python are zero-indexed meaning that the first element in the list is at index 0, the second element is at index 1 and so on: names = ['Alice', 'Bob', 'Craig', 'Diana', 'Eric'] print(names[0]) # Alice print(names[2]) # Craig

Indices can also be negative which means counting from the end of the list (-1 being the index of the last element). So, using the list from the above example: print(names[-1]) # Eric print(names[-4]) # Bob

Lists are mutable, so you can change the values in a list: names[0] = 'Ann' print(names) # Outputs ['Ann', 'Bob', 'Craig', 'Diana', 'Eric']

Besides, it is possible to add and/or remove elements from a list: Append object to end of list with L.append(object), returns None. names = ['Alice', 'Bob', 'Craig', 'Diana', 'Eric'] names.append("Sia") print(names) # Outputs ['Alice', 'Bob', 'Craig', 'Diana', 'Eric', 'Sia']

Add a new element to list at a specific index. L.insert(index, object) names.insert(1, "Nikki") print(names) # Outputs ['Alice', 'Nikki', 'Bob', 'Craig', 'Diana', 'Eric', 'Sia']

Remove the first occurrence of a value with L.remove(value), returns None names.remove("Bob") print(names) # Outputs ['Alice', 'Nikki', 'Craig', 'Diana', 'Eric', 'Sia']

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Get the index in the list of the first item whose value is x. It will show an error if there is no such item. name.index("Alice") 0

Count length of list len(names) 6

count occurrence of any item in list a = [1, 1, 1, 2, 3, 4] a.count(1) 3

Reverse the list a.reverse() [4, 3, 2, 1, 1, 1] # or a[::-1] [4, 3, 2, 1, 1, 1]

Remove and return item at index (defaults to the last item) with L.pop([index]), returns the item names.pop() # Outputs 'Sia'

You can iterate over the list elements like below: for element in my_list: print (element)

Tuples A tuple is similar to a list except that it is fixed-length and immutable. So the values in the tuple cannot be changed nor the values be added to or removed from the tuple. Tuples are commonly used for small collections of values that will not need to change, such as an IP address and port. Tuples are represented with parentheses instead of square brackets: ip_address = ('10.20.30.40', 8080)

The same indexing rules for lists also apply to tuples. Tuples can also be nested and the values can be any valid Python valid. A tuple with only one member must be defined (note the comma) this way: one_member_tuple = ('Only member',)

or one_member_tuple = 'Only member',

# No brackets

or just using tuple syntax

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one_member_tuple = tuple(['Only member'])

Dictionaries A dictionary in Python is a collection of key-value pairs. The dictionary is surrounded by curly braces. Each pair is separated by a comma and the key and value are separated by a colon. Here is an example: state_capitals = { 'Arkansas': 'Little Rock', 'Colorado': 'Denver', 'California': 'Sacramento', 'Georgia': 'Atlanta' }

To get a value, refer to it by its key: ca_capital = state_capitals['California']

You can also get all of the keys in a dictionary and then iterate over them: for k in state_capitals.keys(): print('{} is the capital of {}'.format(state_capitals[k], k))

Dictionaries strongly resemble JSON syntax. The native json module in the Python standard library can be used to convert between JSON and dictionaries. set A set is a collection of elements with no repeats and without insertion order but sorted order. They are used in situations where it is only important that some things are grouped together, and not what order they were included. For large groups of data, it is much faster to check whether or not an element is in a set than it is to do the same for a list. Defining a set is very similar to defining a dictionary: first_names = {'Adam', 'Beth', 'Charlie'}

Or you can build a set using an existing list: my_list = [1,2,3] my_set = set(my_list)

Check membership of the set using in: if name in first_names: print(name)

You can iterate over a set exactly like a list, but remember: the values will be in an arbitrary, implementationdefined order. defaultdict A defaultdict is a dictionary with a default value for keys, so that keys for which no value has been explicitly defined can be accessed without errors. defaultdict is especially useful when the values in the dictionary are collections (lists, dicts, etc) in the sense that it does not need to be initialized every time when a new key is used. GoalKicker.com – Python® Notes for Professionals

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A defaultdict will never raise a KeyError. Any key that does not exist gets the default value returned. For example, consider the following dictionary >>> state_capitals = { 'Arkansas': 'Little Rock', 'Colorado': 'Denver', 'California': 'Sacramento', 'Georgia': 'Atlanta' }

If we try to access a non-existent key, python returns us an error as follows >>> state_capitals['Alabama'] Traceback (most recent call last): File "", line 1, in state_capitals['Alabama'] KeyError: 'Alabama'

Let us try with a defaultdict. It can be found in the collections module. >>> from collections import defaultdict >>> state_capitals = defaultdict(lambda: 'Boston')

What we did here is to set a default value (Boston) in case the give key does not exist. Now populate the dict as before: >>> >>> >>> >>>

state_capitals['Arkansas'] = 'Little Rock' state_capitals['California'] = 'Sacramento' state_capitals['Colorado'] = 'Denver' state_capitals['Georgia'] = 'Atlanta'

If we try to access the dict with a non-existent key, python will return us the default value i.e. Boston >>> state_capitals['Alabama'] 'Boston'

and returns the created values for existing key just like a normal dictionary >>> state_capitals['Arkansas'] 'Little Rock'

Section 1.6: IDLE - Python GUI IDLE is Python’s Integrated Development and Learning Environment and is an alternative to the command line. As the name may imply, IDLE is very useful for developing new code or learning python. On Windows this comes with the Python interpreter, but in other operating systems you may need to install it through your package manager. The main purposes of IDLE are: Multi-window text editor with syntax highlighting, autocompletion, and smart indent Python shell with syntax highlighting Integrated debugger with stepping, persistent breakpoints, and call stack visibility Automatic indentation (useful for beginners learning about Python's indentation) GoalKicker.com – Python® Notes for Professionals

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Saving the Python program as .py files and run them and edit them later at any them using IDLE. In IDLE, hit F5 or run Python Shell to launch an interpreter. Using IDLE can be a better learning experience for new users because code is interpreted as the user writes. Note that there are lots of alternatives, see for example this discussion or this list. Troubleshooting Windows If you're on Windows, the default command is python. If you receive a "'python' is not recognized" error, the most likely cause is that Python's location is not in your system's PATH environment variable. This can be accessed by right-clicking on 'My Computer' and selecting 'Properties' or by navigating to 'System' through 'Control Panel'. Click on 'Advanced system settings' and then 'Environment Variables...'. Edit the PATH variable to include the directory of your Python installation, as well as the Script folder (usually C:\Python27;C:\Python27\Scripts). This requires administrative privileges and may require a restart.

When using multiple versions of Python on the same machine, a possible solution is to rename one of the python.exe files. For example, naming one version python27.exe would cause python27 to become the

Python command for that version. You can also use the Python Launcher for Windows, which is available through the installer and comes by default. It allows you to select the version of Python to run by using py -[x.y] instead of python[x.y]. You can use the latest version of Python 2 by running scripts with py -2 and the latest version of Python 3 by running scripts with py -3.

Debian/Ubuntu/MacOS This section assumes that the location of the python executable has been added to the PATH environment variable. If you're on Debian/Ubuntu/MacOS, open the terminal and type python for Python 2.x or python3 for Python 3.x. Type which python to see which Python interpreter will be used.

Arch Linux The default Python on Arch Linux (and descendants) is Python 3, so use python or python3 for Python 3.x and python2 for Python 2.x.

Other systems Python 3 is sometimes bound to python instead of python3. To use Python 2 on these systems where it is installed, you can use python2.

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Section 1.7: User Input Interactive input To get input from the user, use the input function (note: in Python 2.x, the function is called raw_input instead, although Python 2.x has its own version of input that is completely different): Python 2.x Version

≥ 2.3

name = raw_input("What is your name? ") # Out: What is your name? _

Security Remark Do not use input() in Python2 - the entered text will be evaluated as if it were a Python expression (equivalent to eval(input()) in Python3), which might easily become a vulnerability. See this article for further information on the risks of using this function. Python 3.x Version

≥ 3.0

name = input("What is your name? ") # Out: What is your name? _

The remainder of this example will be using Python 3 syntax. The function takes a string argument, which displays it as a prompt and returns a string. The above code provides a prompt, waiting for the user to input. name = input("What is your name? ") # Out: What is your name?

If the user types "Bob" and hits enter, the variable name will be assigned to the string "Bob": name = input("What is your name? ") # Out: What is your name? Bob print(name) # Out: Bob

Note that the input is always of type str, which is important if you want the user to enter numbers. Therefore, you need to convert the str before trying to use it as a number: x = input("Write a number:") # Out: Write a number: 10 x / 2 # Out: TypeError: unsupported operand type(s) for /: 'str' and 'int' float(x) / 2 # Out: 5.0

NB: It's recommended to use try/except blocks to catch exceptions when dealing with user inputs. For instance, if your code wants to cast a raw_input into an int, and what the user writes is uncastable, it raises a ValueError.

Section 1.8: Built in Modules and Functions A module is a file containing Python definitions and statements. Function is a piece of code which execute some logic. >>> pow(2,3)

#8

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To check the built in function in python we can use dir(). If called without an argument, return the names in the current scope. Else, return an alphabetized list of names comprising (some of) the attribute of the given object, and of attributes reachable from it. >>> dir(__builtins__) [ 'ArithmeticError', 'AssertionError', 'AttributeError', 'BaseException', 'BufferError', 'BytesWarning', 'DeprecationWarning', 'EOFError', 'Ellipsis', 'EnvironmentError', 'Exception', 'False', 'FloatingPointError', 'FutureWarning', 'GeneratorExit', 'IOError', 'ImportError', 'ImportWarning', 'IndentationError', 'IndexError', 'KeyError', 'KeyboardInterrupt', 'LookupError', 'MemoryError', 'NameError', 'None', 'NotImplemented', 'NotImplementedError', 'OSError', 'OverflowError', 'PendingDeprecationWarning', 'ReferenceError', 'RuntimeError', 'RuntimeWarning', 'StandardError', 'StopIteration', 'SyntaxError', 'SyntaxWarning', 'SystemError', 'SystemExit', 'TabError', 'True', 'TypeError', 'UnboundLocalError', 'UnicodeDecodeError', 'UnicodeEncodeError', 'UnicodeError', 'UnicodeTranslateError', 'UnicodeWarning', 'UserWarning', 'ValueError', 'Warning', 'ZeroDivisionError', '__debug__', '__doc__',

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'__import__', '__name__', '__package__', 'abs', 'all', 'any', 'apply', 'basestring', 'bin', 'bool', 'buffer', 'bytearray', 'bytes', 'callable', 'chr', 'classmethod', 'cmp', 'coerce', 'compile', 'complex', 'copyright', 'credits', 'delattr', 'dict', 'dir', 'divmod', 'enumerate', 'eval', 'execfile', 'exit', 'file', 'filter', 'float', 'format', 'frozenset', 'getattr', 'globals', 'hasattr', 'hash', 'help', 'hex', 'id', 'input', 'int', 'intern', 'isinstance', 'issubclass', 'iter', 'len', 'license', 'list', 'locals', 'long', 'map', 'max', 'memoryview', 'min', 'next', 'object', 'oct', 'open', 'ord',

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'pow', 'print', 'property', 'quit', 'range', 'raw_input', 'reduce', 'reload', 'repr', 'reversed', 'round', 'set', 'setattr', 'slice', 'sorted', 'staticmethod', 'str', 'sum', 'super', 'tuple', 'type', 'unichr', 'unicode', 'vars', 'xrange', 'zip' ]

To know the functionality of any function, we can use built in function help . >>> help(max) Help on built-in function max in module __builtin__: max(...) max(iterable[, key=func]) -> value max(a, b, c, ...[, key=func]) -> value With a single iterable argument, return its largest item. With two or more arguments, return the largest argument.

Built in modules contains extra functionalities. For example to get square root of a number we need to include math module. >>> import math >>> math.sqrt(16) # 4.0

To know all the functions in a module we can assign the functions list to a variable, and then print the variable. >>> import math >>> dir(math) ['__doc__', '__name__', '__package__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', 'cos', 'cosh', 'degrees', 'e', 'erf', 'erfc', 'exp', 'expm1', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'gamma', 'hypot', 'isinf', 'isnan', 'ldexp', 'lgamma', 'log', 'log10', 'log1p', 'modf', 'pi', 'pow', 'radians', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trunc']

it seems __doc__ is useful to provide some documentation in, say, functions

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>>> math.__doc__ 'This module is always available. It provides access to the\nmathematical functions defined by the C standard.'

In addition to functions, documentation can also be provided in modules. So, if you have a file named helloWorld.py like this: """This is the module docstring.""" def sayHello(): """This is the function docstring.""" return 'Hello World'

You can access its docstrings like this: >>> import helloWorld >>> helloWorld.__doc__ 'This is the module docstring.' >>> helloWorld.sayHello.__doc__ 'This is the function docstring.'

For any user defined type, its attributes, its class's attributes, and recursively the attributes of its class's base classes can be retrieved using dir() >>> class MyClassObject(object): ... pass ... >>> dir(MyClassObject) ['__class__', '__delattr__', '__dict__', '__doc__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__']

Any data type can be simply converted to string using a builtin function called str. This function is called by default when a data type is passed to print >>> str(123)

# "123"

Section 1.9: Creating a module A module is an importable file containing definitions and statements. A module can be created by creating a .py file. # hello.py def say_hello(): print("Hello!")

Functions in a module can be used by importing the module. For modules that you have made, they will need to be in the same directory as the file that you are importing them into. (However, you can also put them into the Python lib directory with the pre-included modules, but should be avoided if possible.) $ python >>> import hello >>> hello.say_hello()

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=> "Hello!"

Modules can be imported by other modules. # greet.py import hello hello.say_hello()

Specific functions of a module can be imported. # greet.py from hello import say_hello say_hello()

Modules can be aliased. # greet.py import hello as ai ai.say_hello()

A module can be stand-alone runnable script. # run_hello.py if __name__ == '__main__': from hello import say_hello say_hello()

Run it! $ python run_hello.py => "Hello!"

If the module is inside a directory and needs to be detected by python, the directory should contain a file named __init__.py.

Section 1.10: Installation of Python 2.7.x and 3.x Note: Following instructions are written for Python 2.7 (unless specified): instructions for Python 3.x are similar. Windows First, download the latest version of Python 2.7 from the official Website (https://www.python.org/downloads/). Version is provided as an MSI package. To install it manually, just double-click the file. By default, Python installs to a directory: C:\Python27\

Warning: installation does not automatically modify the PATH environment variable. Assuming that your Python installation is in C:\Python27, add this to your PATH:

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C:\Python27\;C:\Python27\Scripts\

Now to check if Python installation is valid write in cmd: python --version

Python 2.x and 3.x Side-By-Side To install and use both Python 2.x and 3.x side-by-side on a Windows machine: 1. Install Python 2.x using the MSI installer. Ensure Python is installed for all users. Optional: add Python to PATH to make Python 2.x callable from the command-line using python. 2. Install Python 3.x using its respective installer. Again, ensure Python is installed for all users. Optional: add Python to PATH to make Python 3.x callable from the command-line using python. This may override Python 2.x PATH settings, so double-check your PATH and ensure it's configured to your preferences. Make sure to install the py launcher for all users. Python 3 will install the Python launcher which can be used to launch Python 2.x and Python 3.x interchangeably from the command-line: P:\>py -3 Python 3.6.1 (v3.6.1:69c0db5, Mar 21 2017, 17:54:52) [MSC v.1900 32 bit (Intel)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>> C:\>py -2 Python 2.7.13 (v2.7.13:a06454b1afa1, Dec 17 2016, 20:42:59) [MSC v.1500 32 Intel)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>>

To use the corresponding version of pip for a specific Python version, use: C:\>py -3 -m pip -V pip 9.0.1 from C:\Python36\lib\site-packages (python 3.6) C:\>py -2 -m pip -V pip 9.0.1 from C:\Python27\lib\site-packages (python 2.7)

Linux The latest versions of CentOS, Fedora, Red Hat Enterprise (RHEL) and Ubuntu come with Python 2.7. To install Python 2.7 on linux manually, just do the following in terminal: wget --no-check-certificate https://www.python.org/ftp/python/2.7.X/Python-2.7.X.tgz tar -xzf Python-2.7.X.tgz cd Python-2.7.X ./configure make

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sudo make install

Also add the path of new python in PATH environment variable. If new python is in /root/python-2.7.X then run export PATH = $PATH:/root/python-2.7.X

Now to check if Python installation is valid write in terminal: python --version

Ubuntu (From Source) If you need Python 3.6 you can install it from source as shown below (Ubuntu 16.10 and 17.04 have 3.6 version in the universal repository). Below steps have to be followed for Ubuntu 16.04 and lower versions: sudo apt install build-essential checkinstall sudo apt install libreadline-gplv2-dev libncursesw5-dev libssl-dev libsqlite3-dev tk-dev libgdbmdev libc6-dev libbz2-dev wget https://www.python.org/ftp/python/3.6.1/Python-3.6.1.tar.xz tar xvf Python-3.6.1.tar.xz cd Python-3.6.1/ ./configure --enable-optimizations sudo make altinstall

macOS As we speak, macOS comes installed with Python 2.7.10, but this version is outdated and slightly modified from the regular Python. The version of Python that ships with OS X is great for learning but it’s not good for development. The version shipped with OS X may be out of date from the official current Python release, which is considered the stable production version. (source) Install Homebrew: /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

Install Python 2.7: brew install python

For Python 3.x, use the command brew install python3 instead.

Section 1.11: String function - str() and repr() There are two functions that can be used to obtain a readable representation of an object. repr(x) calls x.__repr__(): a representation of x. eval will usually convert the result of this function back to the

original object. str(x) calls x.__str__(): a human-readable string that describes the object. This may elide some technical detail.

repr()

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For many types, this function makes an attempt to return a string that would yield an object with the same value when passed to eval(). Otherwise, the representation is a string enclosed in angle brackets that contains the name of the type of the object along with additional information. This often includes the name and address of the object. str() For strings, this returns the string itself. The difference between this and repr(object) is that str(object) does not always attempt to return a string that is acceptable to eval(). Rather, its goal is to return a printable or 'human readable' string. If no argument is given, this returns the empty string, ''. Example 1: s = """w'o"w""" repr(s) # Output: '\'w\\\'o"w\'' str(s) # Output: 'w\'o"w' eval(str(s)) == s # Gives a SyntaxError eval(repr(s)) == s # Output: True

Example 2: import datetime today = datetime.datetime.now() str(today) # Output: '2016-09-15 06:58:46.915000' repr(today) # Output: 'datetime.datetime(2016, 9, 15, 6, 58, 46, 915000)'

When writing a class, you can override these methods to do whatever you want: class Represent(object): def __init__(self, x, y): self.x, self.y = x, y def __repr__(self): return "Represent(x={},y=\"{}\")".format(self.x, self.y) def __str__(self): return "Representing x as {} and y as {}".format(self.x, self.y)

Using the above class we can see the results: r = Represent(1, "Hopper") print(r) # prints __str__ print(r.__repr__) # prints __repr__: '' rep = r.__repr__() # sets the execution of __repr__ to a new variable print(rep) # prints 'Represent(x=1,y="Hopper")' r2 = eval(rep) # evaluates rep print(r2) # prints __str__ from new object print(r2 == r) # prints 'False' because they are different objects

Section 1.12: Installing external modules using pip pip is your friend when you need to install any package from the plethora of choices available at the python

package index (PyPI). pip is already installed if you're using Python 2 >= 2.7.9 or Python 3 >= 3.4 downloaded from python.org. For computers running Linux or another *nix with a native package manager, pip must often be manually installed.

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On instances with both Python 2 and Python 3 installed, pip often refers to Python 2 and pip3 to Python 3. Using pip will only install packages for Python 2 and pip3 will only install packages for Python 3.

Finding / installing a package Searching for a package is as simple as typing $ pip search # Searches for packages whose name or summary contains

Installing a package is as simple as typing (in a terminal / command-prompt, not in the Python interpreter) $ pip install [package_name]

# latest version of the package

$ pip install [package_name]==x.x.x

# specific version of the package

$ pip install '[package_name]>=x.x.x'

# minimum version of the package

where x.x.x is the version number of the package you want to install. When your server is behind proxy, you can install package by using below command: $ pip --proxy http://: install

Upgrading installed packages When new versions of installed packages appear they are not automatically installed to your system. To get an overview of which of your installed packages have become outdated, run: $ pip list --outdated

To upgrade a specific package use $ pip install [package_name] --upgrade

Updating all outdated packages is not a standard functionality of pip. Upgrading pip You can upgrade your existing pip installation by using the following commands On Linux or macOS X: $ pip install -U pip

You may need to use sudo with pip on some Linux Systems On Windows: py -m pip install -U pip

or python -m pip install -U pip

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For more information regarding pip do read here.

Section 1.13: Help Utility Python has several functions built into the interpreter. If you want to get information of keywords, built-in functions, modules or topics open a Python console and enter: >>> help()

You will receive information by entering keywords directly: >>> help(help)

or within the utility: help> help

which will show an explanation: Help on _Helper in module _sitebuiltins object: class _Helper(builtins.object) | Define the builtin 'help'. | | This is a wrapper around pydoc.help that provides a helpful message | when 'help' is typed at the Python interactive prompt. | | Calling help() at the Python prompt starts an interactive help session. | Calling help(thing) prints help for the python object 'thing'. | | Methods defined here: | | __call__(self, *args, **kwds) | | __repr__(self) | | ---------------------------------------------------------------------| Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined)

You can also request subclasses of modules: help(pymysql.connections)

You can use help to access the docstrings of the different modules you have imported, e.g., try the following: >>> help(math)

and you'll get an error >>> import math

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>>> help(math)

And now you will get a list of the available methods in the module, but only AFTER you have imported it. Close the helper with quit

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Chapter 2: Python Data Types Data types are nothing but variables you use to reserve some space in memory. Python variables do not need an explicit declaration to reserve memory space. The declaration happens automatically when you assign a value to a variable.

Section 2.1: String Data Type String are identified as a contiguous set of characters represented in the quotation marks. Python allows for either pairs of single or double quotes. Strings are immutable sequence data type, i.e each time one makes any changes to a string, completely new string object is created. a_str = 'Hello World' print(a_str) #output will be whole string. Hello World print(a_str[0]) #output will be first character. H print(a_str[0:5]) #output will be first five characters. Hello

Section 2.2: Set Data Types Sets are unordered collections of unique objects, there are two types of set: 1. Sets - They are mutable and new elements can be added once sets are defined basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'} print(basket) # duplicates will be removed > {'orange', 'banana', 'pear', 'apple'} a = set('abracadabra') print(a) # unique letters in a > {'a', 'r', 'b', 'c', 'd'} a.add('z') print(a) > {'a', 'c', 'r', 'b', 'z', 'd'}

2. Frozen Sets - They are immutable and new elements cannot added after its defined. b = frozenset('asdfagsa') print(b) > frozenset({'f', 'g', 'd', 'a', 's'}) cities = frozenset(["Frankfurt", "Basel","Freiburg"]) print(cities) > frozenset({'Frankfurt', 'Basel', 'Freiburg'})

Section 2.3: Numbers data type Numbers have four types in Python. Int, float, complex, and long. int_num = 10 #int value float_num = 10.2 #float value complex_num = 3.14j #complex value long_num = 1234567L #long value

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Section 2.4: List Data Type A list contains items separated by commas and enclosed within square brackets [].lists are almost similar to arrays in C. One difference is that all the items belonging to a list can be of different data type. list = [123,'abcd',10.2,'d'] #can be an array of any data type or single data type. list1 = ['hello','world'] print(list) #will output whole list. [123,'abcd',10.2,'d'] print(list[0:2]) #will output first two element of list. [123,'abcd'] print(list1 * 2) #will gave list1 two times. ['hello','world','hello','world'] print(list + list1) #will gave concatenation of both the lists. [123,'abcd',10.2,'d','hello','world']

Section 2.5: Dictionary Data Type Dictionary consists of key-value pairs. It is enclosed by curly braces {} and values can be assigned and accessed using square brackets[]. dic={'name':'red','age':10} print(dic) #will output all the key-value pairs. {'name':'red','age':10} print(dic['name']) #will output only value with 'name' key. 'red' print(dic.values()) #will output list of values in dic. ['red',10] print(dic.keys()) #will output list of keys. ['name','age']

Section 2.6: Tuple Data Type Lists are enclosed in brackets [ ] and their elements and size can be changed, while tuples are enclosed in parentheses ( ) and cannot be updated. Tuples are immutable. tuple = (123,'hello') tuple1 = ('world') print(tuple) #will output whole tuple. (123,'hello') print(tuple[0]) #will output first value. (123) print(tuple + tuple1) #will output (123,'hello','world') tuple[1]='update' #this will give you error.

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Chapter 3: Indentation Section 3.1: Simple example For Python, Guido van Rossum based the grouping of statements on indentation. The reasons for this are explained in the first section of the "Design and History Python FAQ". Colons, :, are used to declare an indented code block, such as the following example: class ExampleClass: #Every function belonging to a class must be indented equally def __init__(self): name = "example" def someFunction(self, a): #Notice everything belonging to a function must be indented if a > 5: return True else: return False #If a function is not indented to the same level it will not be considers as part of the parent class def separateFunction(b): for i in b: #Loops are also indented and nested conditions start a new indentation if i == 1: return True return False separateFunction([2,3,5,6,1])

Spaces or Tabs? The recommended indentation is 4 spaces but tabs or spaces can be used so long as they are consistent. Do not mix tabs and spaces in Python as this will cause an error in Python 3 and can causes errors in Python 2.

Section 3.2: How Indentation is Parsed Whitespace is handled by the lexical analyzer before being parsed. The lexical analyzer uses a stack to store indentation levels. At the beginning, the stack contains just the value 0, which is the leftmost position. Whenever a nested block begins, the new indentation level is pushed on the stack, and an "INDENT" token is inserted into the token stream which is passed to the parser. There can never be more than one "INDENT" token in a row (IndentationError). When a line is encountered with a smaller indentation level, values are popped from the stack until a value is on top which is equal to the new indentation level (if none is found, a syntax error occurs). For each value popped, a "DEDENT" token is generated. Obviously, there can be multiple "DEDENT" tokens in a row. The lexical analyzer skips empty lines (those containing only whitespace and possibly comments), and will never generate either "INDENT" or "DEDENT" tokens for them. At the end of the source code, "DEDENT" tokens are generated for each indentation level left on the stack, until just the 0 is left. For example: GoalKicker.com – Python® Notes for Professionals

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if foo: if bar: x = 42 else: print foo

is analyzed as:

[0] [0, 4] [0, 4, 8] [0] [0, 2]

The parser than handles the "INDENT" and "DEDENT" tokens as block delimiters.

Section 3.3: Indentation Errors The spacing should be even and uniform throughout. Improper indentation can cause an IndentationError or cause the program to do something unexpected. The following example raises an IndentationError: a = 7 if a > 5: print "foo" else: print "bar" print "done"

Or if the line following a colon is not indented, an IndentationError will also be raised: if True: print "true"

If you add indentation where it doesn't belong, an IndentationError will be raised: if

True: a = 6 b = 5

If you forget to un-indent functionality could be lost. In this example None is returned instead of the expected False: def isEven(a): if a%2 ==0: return True #this next line should be even with the if return False print isEven(7)

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Chapter 4: Comments and Documentation Section 4.1: Single line, inline and multiline comments Comments are used to explain code when the basic code itself isn't clear. Python ignores comments, and so will not execute code in there, or raise syntax errors for plain English sentences. Single-line comments begin with the hash character (#) and are terminated by the end of line. Single line comment: # This is a single line comment in Python

Inline comment: print("Hello World")

# This line prints "Hello World"

Comments spanning multiple lines have """ or ''' on either end. This is the same as a multiline string, but they can be used as comments: """ This type of comment spans multiple lines. These are mostly used for documentation of functions, classes and modules. """

Section 4.2: Programmatically accessing docstrings Docstrings are - unlike regular comments - stored as an attribute of the function they document, meaning that you can access them programmatically. An example function def func(): """This is a function that does nothing at all""" return

The docstring can be accessed using the __doc__ attribute: print(func.__doc__)

This is a function that does nothing at all

help(func)

Help on function func in module __main__: func()

This is a function that does nothing at all Another example function

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function.__doc__ is just the actual docstring as a string, while the help function provides general information

about a function, including the docstring. Here's a more helpful example: def greet(name, greeting="Hello"): """Print a greeting to the user `name` Optional parameter `greeting` can change what they're greeted with.""" print("{} {}".format(greeting, name)) help(greet)

Help on function greet in module __main__: greet(name, greeting='Hello')

Print a greeting to the user name Optional parameter greeting can change what they're greeted with. Advantages of docstrings over regular comments Just putting no docstring or a regular comment in a function makes it a lot less helpful. def greet(name, greeting="Hello"): # Print a greeting to the user `name` # Optional parameter `greeting` can change what they're greeted with. print("{} {}".format(greeting, name)) print(greet.__doc__)

None

help(greet)

Help on function greet in module main: greet(name, greeting='Hello')

Section 4.3: Write documentation using docstrings A docstring is a multi-line comment used to document modules, classes, functions and methods. It has to be the first statement of the component it describes. def hello(name): """Greet someone. Print a greeting ("Hello") for the person with the given name. """ print("Hello "+name) class Greeter: """An object used to greet people.

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It contains multiple greeting functions for several languages and times of the day. """

The value of the docstring can be accessed within the program and is - for example - used by the help command. Syntax conventions PEP 257 PEP 257 defines a syntax standard for docstring comments. It basically allows two types: One-line Docstrings: According to PEP 257, they should be used with short and simple functions. Everything is placed in one line, e.g: def hello(): """Say hello to your friends.""" print("Hello my friends!")

The docstring shall end with a period, the verb should be in the imperative form. Multi-line Docstrings: Multi-line docstring should be used for longer, more complex functions, modules or classes. def hello(name, language="en"): """Say hello to a person. Arguments: name: the name of the person language: the language in which the person should be greeted """ print(greeting[language]+" "+name)

They start with a short summary (equivalent to the content of a one-line docstring) which can be on the same line as the quotation marks or on the next line, give additional detail and list parameters and return values. Note PEP 257 defines what information should be given within a docstring, it doesn't define in which format it should be given. This was the reason for other parties and documentation parsing tools to specify their own standards for documentation, some of which are listed below and in this question. Sphinx Sphinx is a tool to generate HTML based documentation for Python projects based on docstrings. Its markup language used is reStructuredText. They define their own standards for documentation, pythonhosted.org hosts a very good description of them. The Sphinx format is for example used by the pyCharm IDE. A function would be documented like this using the Sphinx/reStructuredText format: def hello(name, language="en"): """Say hello to a person. :param name: the name of the person :type name: str :param language: the language in which the person should be greeted :type language: str

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:return: a number :rtype: int """ print(greeting[language]+" "+name) return 4

Google Python Style Guide Google has published Google Python Style Guide which defines coding conventions for Python, including documentation comments. In comparison to the Sphinx/reST many people say that documentation according to Google's guidelines is better human-readable. The pythonhosted.org page mentioned above also provides some examples for good documentation according to the Google Style Guide. Using the Napoleon plugin, Sphinx can also parse documentation in the Google Style Guide-compliant format. A function would be documented like this using the Google Style Guide format: def hello(name, language="en"): """Say hello to a person. Args: name: the name of the person as string language: the language code string Returns: A number. """ print(greeting[language]+" "+name) return 4

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Chapter 5: Date and Time Section 5.1: Parsing a string into a timezone aware datetime object Python 3.2+ has support for %z format when parsing a string into a datetime object. UTC offset in the form +HHMM or -HHMM (empty string if the object is naive). Python 3.x Version

≥ 3.2

import datetime dt = datetime.datetime.strptime("2016-04-15T08:27:18-0500", "%Y-%m-%dT%H:%M:%S%z")

For other versions of Python, you can use an external library such as dateutil, which makes parsing a string with timezone into a datetime object is quick. import dateutil.parser dt = dateutil.parser.parse("2016-04-15T08:27:18-0500")

The dt variable is now a datetime object with the following value: datetime.datetime(2016, 4, 15, 8, 27, 18, tzinfo=tzoffset(None, -18000))

Section 5.2: Constructing timezone-aware datetimes By default all datetime objects are naive. To make them timezone-aware, you must attach a tzinfo object, which provides the UTC offset and timezone abbreviation as a function of date and time. Fixed Offset Time Zones For time zones that are a fixed offset from UTC, in Python 3.2+, the datetime module provides the timezone class, a concrete implementation of tzinfo, which takes a timedelta and an (optional) name parameter: Python 3.x Version

≥ 3.2

from datetime import datetime, timedelta, timezone JST = timezone(timedelta(hours=+9)) dt = datetime(2015, 1, 1, 12, 0, 0, tzinfo=JST) print(dt) # 2015-01-01 12:00:00+09:00 print(dt.tzname()) # UTC+09:00 dt = datetime(2015, 1, 1, 12, 0, 0, tzinfo=timezone(timedelta(hours=9), 'JST')) print(dt.tzname) # 'JST'

For Python versions before 3.2, it is necessary to use a third party library, such as dateutil. dateutil provides an equivalent class, tzoffset, which (as of version 2.5.3) takes arguments of the form dateutil.tz.tzoffset(tzname, offset), where offset is specified in seconds:

Python 3.x Version

< 3.2

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Python 2.x Version

< 2.7

from datetime import datetime, timedelta from dateutil import tz JST = tz.tzoffset('JST', 9 * 3600) # 3600 seconds per hour dt = datetime(2015, 1, 1, 12, 0, tzinfo=JST) print(dt) # 2015-01-01 12:00:00+09:00 print(dt.tzname) # 'JST'

Zones with daylight savings time For zones with daylight savings time, python standard libraries do not provide a standard class, so it is necessary to use a third party library. pytz and dateutil are popular libraries providing time zone classes. In addition to static time zones, dateutil provides time zone classes that use daylight savings time (see the documentation for the tz module). You can use the tz.gettz() method to get a time zone object, which can then be passed directly to the datetime constructor: from datetime import datetime from dateutil import tz local = tz.gettz() # Local time PT = tz.gettz('US/Pacific') # Pacific time dt_l = datetime(2015, 1, 1, 12, tzinfo=local) # I am in EST dt_pst = datetime(2015, 1, 1, 12, tzinfo=PT) dt_pdt = datetime(2015, 7, 1, 12, tzinfo=PT) # DST is handled automatically print(dt_l) # 2015-01-01 12:00:00-05:00 print(dt_pst) # 2015-01-01 12:00:00-08:00 print(dt_pdt) # 2015-07-01 12:00:00-07:00

CAUTION: As of version 2.5.3, dateutil does not handle ambiguous datetimes correctly, and will always default to the later date. There is no way to construct an object with a dateutil timezone representing, for example 2015-11-01 1:30 EDT-4, since this is during a daylight savings time transition.

All edge cases are handled properly when using pytz, but pytz time zones should not be directly attached to time zones through the constructor. Instead, a pytz time zone should be attached using the time zone's localize method: from datetime import datetime, timedelta import pytz PT = pytz.timezone('US/Pacific') dt_pst = PT.localize(datetime(2015, 1, 1, 12)) dt_pdt = PT.localize(datetime(2015, 11, 1, 0, 30)) print(dt_pst) # 2015-01-01 12:00:00-08:00 print(dt_pdt) # 2015-11-01 00:30:00-07:00

Be aware that if you perform datetime arithmetic on a pytz-aware time zone, you must either perform the calculations in UTC (if you want absolute elapsed time), or you must call normalize() on the result:

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dt_new = dt_pdt + timedelta(hours=3) # This should be 2:30 AM PST print(dt_new) # 2015-11-01 03:30:00-07:00 dt_corrected = PT.normalize(dt_new) print(dt_corrected) # 2015-11-01 02:30:00-08:00

Section 5.3: Computing time dierences the timedelta module comes in handy to compute differences between times: from datetime import datetime, timedelta now = datetime.now() then = datetime(2016, 5, 23) # datetime.datetime(2016, 05, 23, 0, 0, 0)

Specifying time is optional when creating a new datetime object delta = now-then delta is of type timedelta print(delta.days) # 60 print(delta.seconds) # 40826

To get n day's after and n day's before date we could use: n day's after date: def get_n_days_after_date(date_format="%d %B %Y", add_days=120): date_n_days_after = datetime.datetime.now() + timedelta(days=add_days) return date_n_days_after.strftime(date_format)

n day's before date: def get_n_days_before_date(self, date_format="%d %B %Y", days_before=120): date_n_days_ago = datetime.datetime.now() - timedelta(days=days_before) return date_n_days_ago.strftime(date_format)

Section 5.4: Basic datetime objects usage The datetime module contains three primary types of objects - date, time, and datetime. import datetime # Date object today = datetime.date.today() new_year = datetime.date(2017, 01, 01) #datetime.date(2017, 1, 1) # Time object noon = datetime.time(12, 0, 0) #datetime.time(12, 0) # Current datetime now = datetime.datetime.now()

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# Datetime object millenium_turn = datetime.datetime(2000, 1, 1, 0, 0, 0) #datetime.datetime(2000, 1, 1, 0, 0)

Arithmetic operations for these objects are only supported within same datatype and performing simple arithmetic with instances of different types will result in a TypeError. # subtraction of noon from today noon-today Traceback (most recent call last): File "", line 1, in TypeError: unsupported operand type(s) for -: 'datetime.time' and 'datetime.date' However, it is straightforward to convert between types. # Do this instead print('Time since the millenium at midnight: ', datetime.datetime(today.year, today.month, today.day) - millenium_turn) # Or this print('Time since the millenium at noon: ', datetime.datetime.combine(today, noon) - millenium_turn)

Section 5.5: Switching between time zones To switch between time zones, you need datetime objects that are timezone-aware. from datetime import datetime from dateutil import tz utc = tz.tzutc() local = tz.tzlocal() utc_now = datetime.utcnow() utc_now # Not timezone-aware. utc_now = utc_now.replace(tzinfo=utc) utc_now # Timezone-aware. local_now = utc_now.astimezone(local) local_now # Converted to local time.

Section 5.6: Simple date arithmetic Dates don't exist in isolation. It is common that you will need to find the amount of time between dates or determine what the date will be tomorrow. This can be accomplished using timedelta objects import datetime today = datetime.date.today() print('Today:', today) yesterday = today - datetime.timedelta(days=1) print('Yesterday:', yesterday) tomorrow = today + datetime.timedelta(days=1) print('Tomorrow:', tomorrow) print('Time between tomorrow and yesterday:', tomorrow - yesterday)

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This will produce results similar to: Today: 2016-04-15 Yesterday: 2016-04-14 Tomorrow: 2016-04-16 Difference between tomorrow and yesterday: 2 days, 0:00:00

Section 5.7: Converting timestamp to datetime The datetime module can convert a POSIX timestamp to a ITC datetime object. The Epoch is January 1st, 1970 midnight. import time from datetime import datetime seconds_since_epoch=time.time()

#1469182681.709

utc_date=datetime.utcfromtimestamp(seconds_since_epoch) #datetime.datetime(2016, 7, 22, 10, 18, 1, 709000)

Section 5.8: Subtracting months from a date accurately Using the calendar module import calendar from datetime import date def monthdelta(date, delta): m, y = (date.month+delta) % 12, date.year + ((date.month)+delta-1) // 12 if not m: m = 12 d = min(date.day, calendar.monthrange(y, m)[1]) return date.replace(day=d,month=m, year=y) next_month = monthdelta(date.today(), 1) #datetime.date(2016, 10, 23)

Using the dateutils module import datetime import dateutil.relativedelta d = datetime.datetime.strptime("2013-03-31", "%Y-%m-%d") d2 = d - dateutil.relativedelta.relativedelta(months=1) #datetime.datetime(2013, 2, 28, 0, 0)

Section 5.9: Parsing an arbitrary ISO 8601 timestamp with minimal libraries Python has only limited support for parsing ISO 8601 timestamps. For strptime you need to know exactly what format it is in. As a complication the stringification of a datetime is an ISO 8601 timestamp, with space as a separator and 6 digit fraction: str(datetime.datetime(2016, 7, 22, 9, 25, 59, 555555)) # '2016-07-22 09:25:59.555555'

but if the fraction is 0, no fractional part is output

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str(datetime.datetime(2016, 7, 22, 9, 25, 59, 0)) # '2016-07-22 09:25:59'

But these 2 forms need a different format for strptime. Furthermore, strptime' does not support at all parsing minute timezones that have a:in it, thus2016-07-22 09:25:59+0300can be parsed, but the standard format2016-07-22 09:25:59+03:00` cannot.

There is a single-file library called iso8601 which properly parses ISO 8601 timestamps and only them. It supports fractions and timezones, and the T separator all with a single function: import iso8601 iso8601.parse_date('2016-07-22 09:25:59') # datetime.datetime(2016, 7, 22, 9, 25, 59, tzinfo=) iso8601.parse_date('2016-07-22 09:25:59+03:00') # datetime.datetime(2016, 7, 22, 9, 25, 59, tzinfo=) iso8601.parse_date('2016-07-22 09:25:59Z') # datetime.datetime(2016, 7, 22, 9, 25, 59, tzinfo=) iso8601.parse_date('2016-07-22T09:25:59.000111+03:00') # datetime.datetime(2016, 7, 22, 9, 25, 59, 111, tzinfo=)

If no timezone is set, iso8601.parse_date defaults to UTC. The default zone can be changed with default_zone keyword argument. Notably, if this is None instead of the default, then those timestamps that do not have an explicit timezone are returned as naive datetimes instead: iso8601.parse_date('2016-07-22T09:25:59', default_timezone=None) # datetime.datetime(2016, 7, 22, 9, 25, 59) iso8601.parse_date('2016-07-22T09:25:59Z', default_timezone=None) # datetime.datetime(2016, 7, 22, 9, 25, 59, tzinfo=)

Section 5.10: Get an ISO 8601 timestamp Without timezone, with microseconds from datetime import datetime datetime.now().isoformat() # Out: '2016-07-31T23:08:20.886783'

With timezone, with microseconds from datetime import datetime from dateutil.tz import tzlocal datetime.now(tzlocal()).isoformat() # Out: '2016-07-31T23:09:43.535074-07:00'

With timezone, without microseconds from datetime import datetime from dateutil.tz import tzlocal datetime.now(tzlocal()).replace(microsecond=0).isoformat() # Out: '2016-07-31T23:10:30-07:00'

See ISO 8601 for more information about the ISO 8601 format.

Section 5.11: Parsing a string with a short time zone name into GoalKicker.com – Python® Notes for Professionals

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a timezone aware datetime object Using the dateutil library as in the previous example on parsing timezone-aware timestamps, it is also possible to parse timestamps with a specified "short" time zone name. For dates formatted with short time zone names or abbreviations, which are generally ambiguous (e.g. CST, which could be Central Standard Time, China Standard Time, Cuba Standard Time, etc - more can be found here) or not necessarily available in a standard database, it is necessary to specify a mapping between time zone abbreviation and tzinfo object. from dateutil import tz from dateutil.parser import parse ET CT MT PT

= = = =

tz.gettz('US/Eastern') tz.gettz('US/Central') tz.gettz('US/Mountain') tz.gettz('US/Pacific')

us_tzinfos = {'CST': 'EST': 'MST': 'PST':

CT, ET, MT, PT,

'CDT': 'EDT': 'MDT': 'PDT':

CT, ET, MT, PT}

dt_est = parse('2014-01-02 04:00:00 EST', tzinfos=us_tzinfos) dt_pst = parse('2016-03-11 16:00:00 PST', tzinfos=us_tzinfos)

After running this: dt_est # datetime.datetime(2014, 1, 2, 4, 0, tzinfo=tzfile('/usr/share/zoneinfo/US/Eastern')) dt_pst # datetime.datetime(2016, 3, 11, 16, 0, tzinfo=tzfile('/usr/share/zoneinfo/US/Pacific'))

It is worth noting that if using a pytz time zone with this method, it will not be properly localized: from dateutil.parser import parse import pytz EST = pytz.timezone('America/New_York') dt = parse('2014-02-03 09:17:00 EST', tzinfos={'EST': EST})

This simply attaches the pytz time zone to the datetime: dt.tzinfo # Will be in Local Mean Time! #

If using this method, you should probably re-localize the naive portion of the datetime after parsing: dt_fixed = dt.tzinfo.localize(dt.replace(tzinfo=None)) dt_fixed.tzinfo # Now it's EST. # )

Section 5.12: Fuzzy datetime parsing (extracting datetime out of a text) It is possible to extract a date out of a text using the dateutil parser in a "fuzzy" mode, where components of the GoalKicker.com – Python® Notes for Professionals

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string not recognized as being part of a date are ignored. from dateutil.parser import parse dt = parse("Today is January 1, 2047 at 8:21:00AM", fuzzy=True) print(dt) dt is now a datetime object and you would see datetime.datetime(2047, 1, 1, 8, 21) printed.

Section 5.13: Iterate over dates Sometimes you want to iterate over a range of dates from a start date to some end date. You can do it using datetime library and timedelta object: import datetime # The size of each step in days day_delta = datetime.timedelta(days=1) start_date = datetime.date.today() end_date = start_date + 7*day_delta for i in range((end_date - start_date).days): print(start_date + i*day_delta)

Which produces: 2016-07-21 2016-07-22 2016-07-23 2016-07-24 2016-07-25 2016-07-26 2016-07-27

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Chapter 6: Date Formatting Section 6.1: Time between two date-times from datetime import datetime a = datetime(2016,10,06,0,0,0) b = datetime(2016,10,01,23,59,59) a-b # datetime.timedelta(4, 1) (a-b).days # 4 (a-b).total_seconds() # 518399.0

Section 6.2: Outputting datetime object to string Uses C standard format codes. from datetime import datetime datetime_for_string = datetime(2016,10,1,0,0) datetime_string_format = '%b %d %Y, %H:%M:%S' datetime.strftime(datetime_for_string,datetime_string_format) # Oct 01 2016, 00:00:00

Section 6.3: Parsing string to datetime object Uses C standard format codes. from datetime import datetime datetime_string = 'Oct 1 2016, 00:00:00' datetime_string_format = '%b %d %Y, %H:%M:%S' datetime.strptime(datetime_string, datetime_string_format) # datetime.datetime(2016, 10, 1, 0, 0)

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Chapter 7: Enum Section 7.1: Creating an enum (Python 2.4 through 3.3) Enums have been backported from Python 3.4 to Python 2.4 through Python 3.3. You can get this the enum34 backport from PyPI. pip install enum34

Creation of an enum is identical to how it works in Python 3.4+ from enum import Enum class Color(Enum): red = 1 green = 2 blue = 3 print(Color.red) # Color.red print(Color(1)) # Color.red print(Color['red']) # Color.red

Section 7.2: Iteration Enums are iterable: class Color(Enum): red = 1 green = 2 blue = 3 [c for c in Color]

# [, , ]

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Chapter 8: Set Section 8.1: Operations on sets with other sets # Intersection {1, 2, 3, 4, 5}.intersection({3, 4, 5, 6}) {1, 2, 3, 4, 5} & {3, 4, 5, 6} # Union {1, 2, 3, 4, 5}.union({3, 4, 5, 6}) {1, 2, 3, 4, 5} | {3, 4, 5, 6} # Difference {1, 2, 3, 4}.difference({2, 3, 5}) {1, 2, 3, 4} - {2, 3, 5}

# {3, 4, 5} # {3, 4, 5}

# {1, 2, 3, 4, 5, 6} # {1, 2, 3, 4, 5, 6}

# {1, 4} # {1, 4}

# Symmetric difference with {1, 2, 3, 4}.symmetric_difference({2, 3, 5}) {1, 2, 3, 4} ^ {2, 3, 5} # Superset check {1, 2}.issuperset({1, 2, 3}) {1, 2} >= {1, 2, 3} # Subset check {1, 2}.issubset({1, 2, 3}) {1, 2} 4 # True 12 < 4 # False 1 < 4 # True

For strings they will compare lexicographically, which is similar to alphabetical order but not quite the same. "alpha" < "beta" # True "gamma" > "beta" # True "gamma" < "OMEGA" # False

In these comparisons, lowercase letters are considered 'greater than' uppercase, which is why "gamma" < "OMEGA" is false. If they were all uppercase it would return the expected alphabetical ordering result: "GAMMA" < "OMEGA" # True

Each type defines it's calculation with the < and > operators differently, so you should investigate what the operators mean with a given type before using it.

Section 15.4: Not equal to x != y

This returns True if x and y are not equal and otherwise returns False. 12 != 1 # True 12 != '12' # True '12' != '12' # False

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Section 15.5: Equal To x == y

This expression evaluates if x and y are the same value and returns the result as a boolean value. Generally both type and value need to match, so the int 12 is not the same as the string '12'. 12 == 12 # True 12 == 1 # False '12' == '12' # True 'spam' == 'spam' # True 'spam' == 'spam ' # False '12' == 12 # False

Note that each type has to define a function that will be used to evaluate if two values are the same. For builtin types these functions behave as you'd expect, and just evaluate things based on being the same value. However custom types could define equality testing as whatever they'd like, including always returning True or always returning False.

Section 15.6: Comparing Objects In order to compare the equality of custom classes, you can override == and != by defining __eq__ and __ne__ methods. You can also override __lt__ (). Note that you only need to override two comparison methods, and Python can handle the rest (== is the same as not < and not >, etc.) class Foo(object): def __init__(self, item): self.my_item = item def __eq__(self, other): return self.my_item == other.my_item a b a a a

= Foo(5) = Foo(5) == b # True != b # False is b # False

Note that this simple comparison assumes that other (the object being compared to) is the same object type. Comparing to another type will throw an error: class Bar(object): def __init__(self, item): self.other_item = item def __eq__(self, other): return self.other_item == other.other_item def __ne__(self, other): return self.other_item != other.other_item c = Bar(5) a == c # throws AttributeError: 'Foo' object has no attribute 'other_item'

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Checking isinstance() or similar will help prevent this (if desired).

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Chapter 16: Loops Parameter Details boolean expression expression that can be evaluated in a boolean context, e.g. x < 10 variable

variable name for the current element from the iterable

iterable

anything that implements iterations

As one of the most basic functions in programming, loops are an important piece to nearly every programming language. Loops enable developers to set certain portions of their code to repeat through a number of loops which are referred to as iterations. This topic covers using multiple types of loops and applications of loops in Python.

Section 16.1: Break and Continue in Loops break statement

When a break statement executes inside a loop, control flow "breaks" out of the loop immediately: i = 0 while i < 7: print(i) if i == 4: print("Breaking from loop") break i += 1

The loop conditional will not be evaluated after the break statement is executed. Note that break statements are only allowed inside loops, syntactically. A break statement inside a function cannot be used to terminate loops that called that function. Executing the following prints every digit until number 4 when the break statement is met and the loop stops: 0 1 2 3 4 Breaking from loop

break statements can also be used inside for loops, the other looping construct provided by Python: for i in (0, 1, 2, 3, 4): print(i) if i == 2: break

Executing this loop now prints: 0 1 2

Note that 3 and 4 are not printed since the loop has ended.

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If a loop has an else clause, it does not execute when the loop is terminated through a break statement. continue statement

A continue statement will skip to the next iteration of the loop bypassing the rest of the current block but continuing the loop. As with break, continue can only appear inside loops: for i in (0, 1, 2, 3, 4, 5): if i == 2 or i == 4: continue print(i) 0 1 3 5

Note that 2 and 4 aren't printed, this is because continue goes to the next iteration instead of continuing on to print(i) when i == 2 or i == 4.

Nested Loops break and continue only operate on a single level of loop. The following example will only break out of the inner for loop, not the outer while loop: while True: for i in range(1,5): if i == 2: break # Will only break out of the inner loop!

Python doesn't have the ability to break out of multiple levels of loop at once -- if this behavior is desired, refactoring one or more loops into a function and replacing break with return may be the way to go. Use return from within a function as a break The return statement exits from a function, without executing the code that comes after it. If you have a loop inside a function, using return from inside that loop is equivalent to having a break as the rest of the code of the loop is not executed (note that any code after the loop is not executed either): def break_loop(): for i in range(1, 5): if (i == 2): return(i) print(i) return(5)

If you have nested loops, the return statement will break all loops: def break_all(): for j in range(1, 5): for i in range(1,4): if i*j == 6: return(i) print(i*j)

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1 2 3 4 2 4 #

# 1*1 # 1*2 # 1*3 # 1*4 # 2*1 # 2*2 return because 2*3 = 6, the remaining iterations of both loops are not executed

Section 16.2: For loops for loops iterate over a collection of items, such as list or dict, and run a block of code with each element from

the collection. for i in [0, 1, 2, 3, 4]: print(i)

The above for loop iterates over a list of numbers. Each iteration sets the value of i to the next element of the list. So first it will be 0, then 1, then 2, etc. The output will be as follow: 0 1 2 3 4

range is a function that returns a series of numbers under an iterable form, thus it can be used in for loops: for i in range(5): print(i)

gives the exact same result as the first for loop. Note that 5 is not printed as the range here is the first five numbers counting from 0. Iterable objects and iterators for loop can iterate on any iterable object which is an object which defines a __getitem__ or a __iter__ function.

The __iter__ function returns an iterator, which is an object with a next function that is used to access the next element of the iterable.

Section 16.3: Iterating over lists To iterate through a list you can use for: for x in ['one', 'two', 'three', 'four']: print(x)

This will print out the elements of the list: one two three four

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The range function generates numbers which are also often used in a for loop. for x in range(1, 6): print(x)

The result will be a special range sequence type in python >=3 and a list in python >> fish = {'name': "Nemo", 'hands': "fins", 'special': "gills"} >>> dog = {'name': "Clifford", 'hands': "paws", 'color': "red"}

Python 3.5+ >>> fishdog = {**fish, **dog} >>> fishdog {'hands': 'paws', 'color': 'red', 'name': 'Clifford', 'special': 'gills'}

As this example demonstrates, duplicate keys map to their lattermost value (for example "Clifford" overrides "Nemo").

Python 3.3+ >>> from collections import ChainMap >>> dict(ChainMap(fish, dog)) {'hands': 'fins', 'color': 'red', 'special': 'gills', 'name': 'Nemo'}

With this technique the foremost value takes precedence for a given key rather than the last ("Clifford" is thrown out in favor of "Nemo").

Python 2.x, 3.x >>> from itertools import chain >>> dict(chain(fish.items(), dog.items())) {'hands': 'paws', 'color': 'red', 'name': 'Clifford', 'special': 'gills'}

This uses the lattermost value, as with the **-based technique for merging ("Clifford" overrides "Nemo"). >>> fish.update(dog) >>> fish {'color': 'red', 'hands': 'paws', 'name': 'Clifford', 'special': 'gills'} dict.update uses the latter dict to overwrite the previous one.

Section 19.6: Accessing keys and values When working with dictionaries, it's often necessary to access all the keys and values in the dictionary, either in a for loop, a list comprehension, or just as a plain list.

Given a dictionary like: mydict = { 'a': '1',

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'b': '2' }

You can get a list of keys using the keys() method: print(mydict.keys()) # Python2: ['a', 'b'] # Python3: dict_keys(['b', 'a'])

If instead you want a list of values, use the values() method: print(mydict.values()) # Python2: ['1', '2'] # Python3: dict_values(['2', '1'])

If you want to work with both the key and its corresponding value, you can use the items() method: print(mydict.items()) # Python2: [('a', '1'), ('b', '2')] # Python3: dict_items([('b', '2'), ('a', '1')])

NOTE: Because a dict is unsorted, keys(), values(), and items() have no sort order. Use sort(), sorted(), or an OrderedDict if you care about the order that these methods return.

Python 2/3 Difference: In Python 3, these methods return special iterable objects, not lists, and are the equivalent of the Python 2 iterkeys(), itervalues(), and iteritems() methods. These objects can be used like lists for the most part, though there are some differences. See PEP 3106 for more details.

Section 19.7: Accessing values of a dictionary dictionary = {"Hello": 1234, "World": 5678} print(dictionary["Hello"])

The above code will print 1234. The string "Hello" in this example is called a key. It is used to lookup a value in the dict by placing the key in square brackets. The number 1234 is seen after the respective colon in the dict definition. This is called the value that "Hello" maps to in this dict. Looking up a value like this with a key that does not exist will raise a KeyError exception, halting execution if uncaught. If we want to access a value without risking a KeyError, we can use the dictionary.get method. By default if the key does not exist, the method will return None. We can pass it a second value to return instead of None in the event of a failed lookup. w = dictionary.get("whatever") x = dictionary.get("whatever", "nuh-uh")

In this example w will get the value None and x will get the value "nuh-uh".

Section 19.8: Creating a dictionary Rules for creating a dictionary:

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Every key must be unique (otherwise it will be overridden) Every key must be hashable (can use the hash function to hash it; otherwise TypeError will be thrown) There is no particular order for the keys. # Creating and populating it with values stock = {'eggs': 5, 'milk': 2} # Or creating an empty dictionary dictionary = {} # And populating it after dictionary['eggs'] = 5 dictionary['milk'] = 2 # Values can also be lists mydict = {'a': [1, 2, 3], 'b': ['one', 'two', 'three']} # Use list.append() method to add new elements to the values list mydict['a'].append(4) # => {'a': [1, 2, 3, 4], 'b': ['one', 'two', 'three']} mydict['b'].append('four') # => {'a': [1, 2, 3, 4], 'b': ['one', 'two', 'three', 'four']} # We can also create a dictionary using a list of two-items tuples iterable = [('eggs', 5), ('milk', 2)] dictionary = dict(iterables) # Or using keyword argument: dictionary = dict(eggs=5, milk=2) # Another way will be to use the dict.fromkeys: dictionary = dict.fromkeys((milk, eggs)) # => {'milk': None, 'eggs': None} dictionary = dict.fromkeys((milk, eggs), (2, 5)) # => {'milk': 2, 'eggs': 5}

Section 19.9: Creating an ordered dictionary You can create an ordered dictionary which will follow a determined order when iterating over the keys in the dictionary. Use OrderedDict from the collections module. This will always return the dictionary elements in the original insertion order when iterated over. from collections import OrderedDict d = OrderedDict() d['first'] = 1 d['second'] = 2 d['third'] = 3 d['last'] = 4 # Outputs "first 1", "second 2", "third 3", "last 4" for key in d: print(key, d[key])

Section 19.10: Unpacking dictionaries using the ** operator You can use the ** keyword argument unpacking operator to deliver the key-value pairs in a dictionary into a function's arguments. A simplified example from the official documentation: >>>

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>>> def parrot(voltage, state, action): ... print("This parrot wouldn't", action, end=' ') ... print("if you put", voltage, "volts through it.", end=' ') ... print("E's", state, "!") ... >>> d = {"voltage": "four million", "state": "bleedin' demised", "action": "VOOM"} >>> parrot(**d) This parrot wouldn't VOOM if you put four million volts through it. E's bleedin' demised !

As of Python 3.5 you can also use this syntax to merge an arbitrary number of dict objects. >>> >>> >>> >>>

fish = {'name': "Nemo", 'hands': "fins", 'special': "gills"} dog = {'name': "Clifford", 'hands': "paws", 'color': "red"} fishdog = {**fish, **dog} fishdog

{'hands': 'paws', 'color': 'red', 'name': 'Clifford', 'special': 'gills'}

As this example demonstrates, duplicate keys map to their lattermost value (for example "Clifford" overrides "Nemo").

Section 19.11: The trailing comma Like lists and tuples, you can include a trailing comma in your dictionary. role = {"By day": "A typical programmer", "By night": "Still a typical programmer", }

PEP 8 dictates that you should leave a space between the trailing comma and the closing brace.

Section 19.12: The dict() constructor The dict() constructor can be used to create dictionaries from keyword arguments, or from a single iterable of key-value pairs, or from a single dictionary and keyword arguments. dict(a=1, b=2, c=3) dict([('d', 4), ('e', 5), ('f', 6)]) dict([('a', 1)], b=2, c=3) dict({'a' : 1, 'b' : 2}, c=3)

# # # #

{'a': {'d': {'a': {'a':

1, 4, 1, 1,

'b': 'e': 'b': 'b':

2, 5, 2, 2,

'c': 'f': 'c': 'c':

3} 6} 3} 3}

Section 19.13: Dictionaries Example Dictionaries map keys to values. car = {} car["wheels"] = 4 car["color"] = "Red" car["model"] = "Corvette"

Dictionary values can be accessed by their keys. print "Little " + car["color"] + " " + car["model"] + "!" # This would print out "Little Red Corvette!"

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car = {"wheels": 4, "color": "Red", "model": "Corvette"}

Dictionary values can be iterated over: for key in car: print key + ": " + car[key] # wheels: 4 # color: Red # model: Corvette

Section 19.14: All combinations of dictionary values options = { "x": ["a", "b"], "y": [10, 20, 30] }

Given a dictionary such as the one shown above, where there is a list representing a set of values to explore for the corresponding key. Suppose you want to explore "x"="a" with "y"=10, then "x"="a" with"y"=10, and so on until you have explored all possible combinations. You can create a list that returns all such combinations of values using the following code. import itertools options = { "x": ["a", "b"], "y": [10, 20, 30]} keys = options.keys() values = (options[key] for key in keys) combinations = [dict(zip(keys, combination)) for combination in itertools.product(*values)] print combinations

This gives us the following list stored in the variable combinations: [{'x': {'x': {'x': {'x': {'x': {'x':

'a', 'b', 'a', 'b', 'a', 'b',

'y': 'y': 'y': 'y': 'y': 'y':

10}, 10}, 20}, 20}, 30}, 30}]

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Chapter 20: List The Python List is a general data structure widely used in Python programs. They are found in other languages, often referred to as dynamic arrays. They are both mutable and a sequence data type that allows them to be indexed and sliced. The list can contain different types of objects, including other list objects.

Section 20.1: List methods and supported operators Starting with a given list a: a = [1, 2, 3, 4, 5]

1. append(value) – appends a new element to the end of the list. # Append values 6, 7, and 7 to the list a.append(6) a.append(7) a.append(7) # a: [1, 2, 3, 4, 5, 6, 7, 7] # Append another list b = [8, 9] a.append(b) # a: [1, 2, 3, 4, 5, 6, 7, 7, [8, 9]] # Append an element of a different type, as list elements do not need to have the same type my_string = "hello world" a.append(my_string) # a: [1, 2, 3, 4, 5, 6, 7, 7, [8, 9], "hello world"]

Note that the append() method only appends one new element to the end of the list. If you append a list to another list, the list that you append becomes a single element at the end of the first list. # Appending a list to another list a = [1, 2, 3, 4, 5, 6, 7, 7] b = [8, 9] a.append(b) # a: [1, 2, 3, 4, 5, 6, 7, 7, [8, 9]] a[8] # Returns: [8,9]

2. extend(enumerable) – extends the list by appending elements from another enumerable. a = [1, 2, 3, 4, 5, 6, 7, 7] b = [8, 9, 10] # Extend list by appending all elements from b a.extend(b) # a: [1, 2, 3, 4, 5, 6, 7, 7, 8, 9, 10] # Extend list with elements from a non-list enumerable: a.extend(range(3)) # a: [1, 2, 3, 4, 5, 6, 7, 7, 8, 9, 10, 0, 1, 2]

Lists can also be concatenated with the + operator. Note that this does not modify any of the original lists:

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a = [1, 2, 3, 4, 5, 6] + [7, 7] + b # a: [1, 2, 3, 4, 5, 6, 7, 7, 8, 9, 10]

3. index(value, [startIndex]) – gets the index of the first occurrence of the input value. If the input value is not in the list a ValueError exception is raised. If a second argument is provided, the search is started at that specified index. a.index(7) # Returns: 6 a.index(49) # ValueError, because 49 is not in a. a.index(7, 7) # Returns: 7 a.index(7, 8) # ValueError, because there is no 7 starting at index 8

4. insert(index, value) – inserts value just before the specified index. Thus after the insertion the new element occupies position index. a.insert(0, 0) # insert 0 at position 0 a.insert(2, 5) # insert 5 at position 2 # a: [0, 1, 5, 2, 3, 4, 5, 6, 7, 7, 8, 9, 10]

5. pop([index]) – removes and returns the item at index. With no argument it removes and returns the last element of the list. a.pop(2) # Returns: 5 # a: [0, 1, 2, 3, 4, 5, 6, 7, 7, 8, 9, 10] a.pop(8) # Returns: 7 # a: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] # With no argument: a.pop() # Returns: 10 # a: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

6. remove(value) – removes the first occurrence of the specified value. If the provided value cannot be found, a ValueError is raised. a.remove(0) a.remove(9) # a: [1, 2, 3, 4, 5, 6, 7, 8] a.remove(10) # ValueError, because 10 is not in a

7. reverse() – reverses the list in-place and returns None. a.reverse() # a: [8, 7, 6, 5, 4, 3, 2, 1]

There are also other ways of reversing a list.

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8. count(value) – counts the number of occurrences of some value in the list. a.count(7) # Returns: 2

9. sort() – sorts the list in numerical and lexicographical order and returns None. a.sort() # a = [1, 2, 3, 4, 5, 6, 7, 8] # Sorts the list in numerical order

Lists can also be reversed when sorted using the reverse=True flag in the sort() method. a.sort(reverse=True) # a = [8, 7, 6, 5, 4, 3, 2, 1]

If you want to sort by attributes of items, you can use the key keyword argument: import datetime class Person(object): def __init__(self, name, birthday, height): self.name = name self.birthday = birthday self.height = height def __repr__(self): return self.name l = [Person("John Cena", datetime.date(1992, 9, 12), 175), Person("Chuck Norris", datetime.date(1990, 8, 28), 180), Person("Jon Skeet", datetime.date(1991, 7, 6), 185)] l.sort(key=lambda item: item.name) # l: [Chuck Norris, John Cena, Jon Skeet] l.sort(key=lambda item: item.birthday) # l: [Chuck Norris, Jon Skeet, John Cena] l.sort(key=lambda item: item.height) # l: [John Cena, Chuck Norris, Jon Skeet]

In case of list of dicts the concept is the same: import datetime l = [{'name':'John Cena', 'birthday': datetime.date(1992, 9, 12),'height': 175}, {'name': 'Chuck Norris', 'birthday': datetime.date(1990, 8, 28),'height': 180}, {'name': 'Jon Skeet', 'birthday': datetime.date(1991, 7, 6), 'height': 185}] l.sort(key=lambda item: item['name']) # l: [Chuck Norris, John Cena, Jon Skeet] l.sort(key=lambda item: item['birthday']) # l: [Chuck Norris, Jon Skeet, John Cena] l.sort(key=lambda item: item['height']) # l: [John Cena, Chuck Norris, Jon Skeet]

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Sort by sub dict: import datetime l = [{'name':'John Cena', 'birthday': datetime.date(1992, 9, 12),'size': {'height': 175, 'weight': 100}}, {'name': 'Chuck Norris', 'birthday': datetime.date(1990, 8, 28),'size' : {'height': 180, 'weight': 90}}, {'name': 'Jon Skeet', 'birthday': datetime.date(1991, 7, 6), 'size': {'height': 185, 'weight': 110}}] l.sort(key=lambda item: item['size']['height']) # l: [John Cena, Chuck Norris, Jon Skeet]

Better way to sort using attrgetter and itemgetter Lists can also be sorted using attrgetter and itemgetter functions from the operator module. These can help improve readability and reusability. Here are some examples, from operator import itemgetter,attrgetter people = [{'name':'chandan','age':20,'salary':2000}, {'name':'chetan','age':18,'salary':5000}, {'name':'guru','age':30,'salary':3000}] by_age = itemgetter('age') by_salary = itemgetter('salary') people.sort(key=by_age) #in-place sorting by age people.sort(key=by_salary) #in-place sorting by salary itemgetter can also be given an index. This is helpful if you want to sort based on indices of a tuple. list_of_tuples = [(1,2), (3,4), (5,0)] list_of_tuples.sort(key=itemgetter(1)) print(list_of_tuples) #[(5, 0), (1, 2), (3, 4)]

Use the attrgetter if you want to sort by attributes of an object, persons = [Person("John Cena", datetime.date(1992, 9, 12), 175), Person("Chuck Norris", datetime.date(1990, 8, 28), 180), Person("Jon Skeet", datetime.date(1991, 7, 6), 185)] #reusing Person class from above example person.sort(key=attrgetter('name')) #sort by name by_birthday = attrgetter('birthday') person.sort(key=by_birthday) #sort by birthday

10. clear() – removes all items from the list a.clear() # a = []

11. Replication – multiplying an existing list by an integer will produce a larger list consisting of that many copies of the original. This can be useful for example for list initialization: b = ["blah"] * 3 # b = ["blah", "blah", "blah"]

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b = [1, 3, 5] * 5 # [1, 3, 5, 1, 3, 5, 1, 3, 5, 1, 3, 5, 1, 3, 5]

Take care doing this if your list contains references to objects (eg a list of lists), see Common Pitfalls - List multiplication and common references. 12. Element deletion – it is possible to delete multiple elements in the list using the del keyword and slice notation: a = del # a del # a del # a

list(range(10)) a[::2] = [1, 3, 5, 7, 9] a[-1] = [1, 3, 5, 7] a[:] = []

13. Copying The default assignment "=" assigns a reference of the original list to the new name. That is, the original name and new name are both pointing to the same list object. Changes made through any of them will be reflected in another. This is often not what you intended. b = a a.append(6) # b: [1, 2, 3, 4, 5, 6]

If you want to create a copy of the list you have below options. You can slice it: new_list = old_list[:]

You can use the built in list() function: new_list = list(old_list)

You can use generic copy.copy(): import copy new_list = copy.copy(old_list) #inserts references to the objects found in the original.

This is a little slower than list() because it has to find out the datatype of old_list first. If the list contains objects and you want to copy them as well, use generic copy.deepcopy(): import copy new_list = copy.deepcopy(old_list) #inserts copies of the objects found in the original.

Obviously the slowest and most memory-needing method, but sometimes unavoidable.

Python 3.x Version

≥ 3.0

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copy() – Returns a shallow copy of the list aa = a.copy() # aa = [1, 2, 3, 4, 5]

Section 20.2: Accessing list values Python lists are zero-indexed, and act like arrays in other languages. lst = [1, 2, 3, 4] lst[0] # 1 lst[1] # 2

Attempting to access an index outside the bounds of the list will raise an IndexError. lst[4]

# IndexError: list index out of range

Negative indices are interpreted as counting from the end of the list. lst[-1] lst[-2] lst[-5]

# 4 # 3 # IndexError: list index out of range

This is functionally equivalent to lst[len(lst)-1]

# 4

Lists allow to use slice notation as lst[start:end:step]. The output of the slice notation is a new list containing elements from index start to end-1. If options are omitted start defaults to beginning of list, end to end of list and step to 1: lst[1:] lst[:3] lst[::2] lst[::-1] lst[-1:0:-1] lst[5:8] lst[1:10]

# # # # # # #

[2, 3, 4] [1, 2, 3] [1, 3] [4, 3, 2, 1] [4, 3, 2] [] since starting index is greater than length of lst, returns empty list [2, 3, 4] same as omitting ending index

With this in mind, you can print a reversed version of the list by calling lst[::-1]

# [4, 3, 2, 1]

When using step lengths of negative amounts, the starting index has to be greater than the ending index otherwise the result will be an empty list. lst[3:1:-1] # [4, 3]

Using negative step indices are equivalent to the following code: reversed(lst)[0:2] # 0 = 1 -1 # 2 = 3 -1

The indices used are 1 less than those used in negative indexing and are reversed.

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Advanced slicing When lists are sliced the __getitem__() method of the list object is called, with a slice object. Python has a builtin slice method to generate slice objects. We can use this to store a slice and reuse it later like so, data = 'chandan purohit 22 2000' name_slice = slice(0,19) age_slice = slice(19,21) salary_slice = slice(22,None)

#assuming data fields of fixed length

#now we can have more readable slices print(data[name_slice]) #chandan purohit print(data[age_slice]) #'22' print(data[salary_slice]) #'2000'

This can be of great use by providing slicing functionality to our objects by overriding __getitem__ in our class.

Section 20.3: Checking if list is empty The emptiness of a list is associated to the boolean False, so you don't have to check len(lst) == 0, but just lst or not lst lst = [] if not lst: print("list is empty") # Output: list is empty

Section 20.4: Iterating over a list Python supports using a for loop directly on a list: my_list = ['foo', 'bar', 'baz'] for item in my_list: print(item) # Output: foo # Output: bar # Output: baz

You can also get the position of each item at the same time: for (index, item) in enumerate(my_list): print('The item in position {} is: {}'.format(index, item)) # Output: The item in position 0 is: foo # Output: The item in position 1 is: bar # Output: The item in position 2 is: baz

The other way of iterating a list based on the index value: for i in range(0,len(my_list)): print(my_list[i]) #output: >>> foo bar

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baz

Note that changing items in a list while iterating on it may have unexpected results: for item in my_list: if item == 'foo': del my_list[0] print(item) # Output: foo # Output: baz

In this last example, we deleted the first item at the first iteration, but that caused bar to be skipped.

Section 20.5: Checking whether an item is in a list Python makes it very simple to check whether an item is in a list. Simply use the in operator. lst = ['test', 'twest', 'tweast', 'treast'] 'test' in lst # Out: True 'toast' in lst # Out: False

Note: the in operator on sets is asymptotically faster than on lists. If you need to use it many times on potentially large lists, you may want to convert your list to a set, and test the presence of elements on the set.

slst = set(lst) 'test' in slst # Out: True

Section 20.6: Any and All You can use all() to determine if all the values in an iterable evaluate to True nums = [1, 1, 0, 1] all(nums) # False chars = ['a', 'b', 'c', 'd'] all(chars) # True

Likewise, any() determines if one or more values in an iterable evaluate to True nums = [1, 1, 0, 1] any(nums) # True vals = [None, None, None, False] any(vals) # False

While this example uses a list, it is important to note these built-ins work with any iterable, including generators. GoalKicker.com – Python® Notes for Professionals

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vals = [1, 2, 3, 4] any(val > 12 for val in vals) # False any((val * 2) > 6 for val in vals) # True

Section 20.7: Reversing list elements You can use the reversed function which returns an iterator to the reversed list: In [3]: rev = reversed(numbers) In [4]: rev Out[4]: [9, 8, 7, 6, 5, 4, 3, 2, 1]

Note that the list "numbers" remains unchanged by this operation, and remains in the same order it was originally. To reverse in place, you can also use the reverse method. You can also reverse a list (actually obtaining a copy, the original list is unaffected) by using the slicing syntax, setting the third argument (the step) as -1: In [1]: numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9] In [2]: numbers[::-1] Out[2]: [9, 8, 7, 6, 5, 4, 3, 2, 1]

Section 20.8: Concatenate and Merge lists 1. The simplest way to concatenate list1 and list2: merged = list1 + list2

2. zip returns a list of tuples, where the i-th tuple contains the i-th element from each of the argument sequences or iterables: alist = ['a1', 'a2', 'a3'] blist = ['b1', 'b2', 'b3'] for a, b in zip(alist, blist): print(a, b) # # # #

Output: a1 b1 a2 b2 a3 b3

If the lists have different lengths then the result will include only as many elements as the shortest one: alist = ['a1', 'a2', 'a3'] blist = ['b1', 'b2', 'b3', 'b4'] for a, b in zip(alist, blist): print(a, b) # Output: # a1 b1

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# a2 b2 # a3 b3 alist = [] len(list(zip(alist, blist))) # Output: # 0

For padding lists of unequal length to the longest one with Nones use itertools.zip_longest (itertools.izip_longest in Python 2) alist = ['a1', 'a2', 'a3'] blist = ['b1'] clist = ['c1', 'c2', 'c3', 'c4'] for a,b,c in itertools.zip_longest(alist, blist, clist): print(a, b, c) # # # # #

Output: a1 b1 c1 a2 None c2 a3 None c3 None None c4

3. Insert to a specific index values: alist = [123, 'xyz', 'zara', 'abc'] alist.insert(3, [2009]) print("Final List :", alist)

Output: Final List : [123, 'xyz', 'zara', 2009, 'abc']

Section 20.9: Length of a list Use len() to get the one-dimensional length of a list. len(['one', 'two'])

# returns 2

len(['one', [2, 3], 'four'])

# returns 3, not 4

len() also works on strings, dictionaries, and other data structures similar to lists.

Note that len() is a built-in function, not a method of a list object. Also note that the cost of len() is O(1), meaning it will take the same amount of time to get the length of a list regardless of its length.

Section 20.10: Remove duplicate values in list Removing duplicate values in a list can be done by converting the list to a set (that is an unordered collection of distinct objects). If a list data structure is needed, then the set can be converted back to a list using the function list():

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names = ["aixk", "duke", "edik", "tofp", "duke"] list(set(names)) # Out: ['duke', 'tofp', 'aixk', 'edik']

Note that by converting a list to a set the original ordering is lost. To preserve the order of the list one can use an OrderedDict import collections >>> collections.OrderedDict.fromkeys(names).keys() # Out: ['aixk', 'duke', 'edik', 'tofp']

Section 20.11: Comparison of lists It's possible to compare lists and other sequences lexicographically using comparison operators. Both operands must be of the same type. [1, 10, 100] < [2, 10, 100] # True, because 1 < 2 [1, 10, 100] < [1, 10, 100] # False, because the lists are equal [1, 10, 100] 2 and x % 2 == 0) else '*' for x in range(10)] # Out:['*', '*', '*', '*', 4, '*', 6, '*', 8, '*']

See also: Filters, which often provide a sufficient alternative to conditional list comprehensions.

Section 21.3: Avoid repetitive and expensive operations using conditional clause Consider the below list comprehension: >>> def f(x): ... import time ... time.sleep(.1) ... return x**2

# Simulate expensive function

>>> [f(x) for x in range(1000) if f(x) > 10] [16, 25, 36, ...]

This results in two calls to f(x) for 1,000 values of x: one call for generating the value and the other for checking the if condition. If f(x) is a particularly expensive operation, this can have significant performance implications.

Worse, if calling f() has side effects, it can have surprising results. Instead, you should evaluate the expensive operation only once for each value of x by generating an intermediate iterable (generator expression) as follows: >>> [v for v in (f(x) for x in range(1000)) if v > 10] [16, 25, 36, ...]

Or, using the builtin map equivalent: >>> [v for v in map(f, range(1000)) if v > 10] [16, 25, 36, ...]

Another way that could result in a more readable code is to put the partial result (v in the previous example) in an iterable (such as a list or a tuple) and then iterate over it. Since v will be the only element in the iterable, the result is that we now have a reference to the output of our slow function computed only once: >>> [v for x in range(1000) for v in [f(x)] if v > 10] [16, 25, 36, ...]

However, in practice, the logic of code can be more complicated and it's important to keep it readable. In general, a separate generator function is recommended over a complex one-liner: >>> def process_prime_numbers(iterable): ... for x in iterable: ... if is_prime(x): ... yield f(x) ... >>> [x for x in process_prime_numbers(range(1000)) if x > 10] [11, 13, 17, 19, ...]

Another way to prevent computing f(x) multiple times is to use the @functools.lru_cache()(Python 3.2+) decorator on f(x). This way since the output of f for the input x has already been computed once, the second GoalKicker.com – Python® Notes for Professionals

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function invocation of the original list comprehension will be as fast as a dictionary lookup. This approach uses memoization to improve efficiency, which is comparable to using generator expressions. Say you have to flatten a list l = [[1, 2, 3], [4, 5, 6], [7], [8, 9]]

Some of the methods could be: reduce(lambda x, y: x+y, l) sum(l, []) list(itertools.chain(*l))

However list comprehension would provide the best time complexity. [item for sublist in l for item in sublist]

The shortcuts based on + (including the implied use in sum) are, of necessity, O(L^2) when there are L sublists -- as the intermediate result list keeps getting longer, at each step a new intermediate result list object gets allocated, and all the items in the previous intermediate result must be copied over (as well as a few new ones added at the end). So (for simplicity and without actual loss of generality) say you have L sublists of I items each: the first I items are copied back and forth L-1 times, the second I items L-2 times, and so on; total number of copies is I times the sum of x for x from 1 to L excluded, i.e., I * (L**2)/2. The list comprehension just generates one list, once, and copies each item over (from its original place of residence to the result list) also exactly once.

Section 21.4: Dictionary Comprehensions A dictionary comprehension is similar to a list comprehension except that it produces a dictionary object instead of a list. A basic example: Python 2.x Version

≥ 2.7

{x: x * x for x in (1, 2, 3, 4)} # Out: {1: 1, 2: 4, 3: 9, 4: 16}

which is just another way of writing: dict((x, x * x) for x in (1, 2, 3, 4)) # Out: {1: 1, 2: 4, 3: 9, 4: 16}

As with a list comprehension, we can use a conditional statement inside the dict comprehension to produce only the dict elements meeting some criterion. Python 2.x Version

≥ 2.7

{name: len(name) for name in ('Stack', 'Overflow', 'Exchange') if len(name) > 6} # Out: {'Exchange': 8, 'Overflow': 8}

Or, rewritten using a generator expression.

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dict((name, len(name)) for name in ('Stack', 'Overflow', 'Exchange') if len(name) > 6) # Out: {'Exchange': 8, 'Overflow': 8}

Starting with a dictionary and using dictionary comprehension as a key-value pair filter Python 2.x Version

≥ 2.7

initial_dict = {'x': 1, 'y': 2} {key: value for key, value in initial_dict.items() if key == 'x'} # Out: {'x': 1}

Switching key and value of dictionary (invert dictionary) If you have a dict containing simple hashable values (duplicate values may have unexpected results): my_dict = {1: 'a', 2: 'b', 3: 'c'}

and you wanted to swap the keys and values you can take several approaches depending on your coding style: swapped = {v: k for k, v in my_dict.items()} swapped = dict((v, k) for k, v in my_dict.iteritems()) swapped = dict(zip(my_dict.values(), my_dict)) swapped = dict(zip(my_dict.values(), my_dict.keys())) swapped = dict(map(reversed, my_dict.items())) print(swapped) # Out: {a: 1, b: 2, c: 3}

Python 2.x Version

≥ 2.3

If your dictionary is large, consider importing itertools and utilize izip or imap. Merging Dictionaries Combine dictionaries and optionally override old values with a nested dictionary comprehension. dict1 = {'w': 1, 'x': 1} dict2 = {'x': 2, 'y': 2, 'z': 2} {k: v for d in [dict1, dict2] for k, v in d.items()} # Out: {'w': 1, 'x': 2, 'y': 2, 'z': 2}

However, dictionary unpacking (PEP 448) may be a preferred. Python 3.x Version

≥ 3.5

{**dict1, **dict2} # Out: {'w': 1, 'x': 2, 'y': 2, 'z': 2}

Note: dictionary comprehensions were added in Python 3.0 and backported to 2.7+, unlike list comprehensions, which were added in 2.0. Versions < 2.7 can use generator expressions and the dict() builtin to simulate the behavior of dictionary comprehensions.

Section 21.5: List Comprehensions with Nested Loops List Comprehensions can use nested for loops. You can code any number of nested for loops within a list comprehension, and each for loop may have an optional associated if test. When doing so, the order of the for GoalKicker.com – Python® Notes for Professionals

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constructs is the same order as when writing a series of nested for statements. The general structure of list comprehensions looks like this: [ expression for target1 in iterable1 [if condition1] for target2 in iterable2 [if condition2]... for targetN in iterableN [if conditionN] ]

For example, the following code flattening a list of lists using multiple for statements: data = [[1, 2], [3, 4], [5, 6]] output = [] for each_list in data: for element in each_list: output.append(element) print(output) # Out: [1, 2, 3, 4, 5, 6]

can be equivalently written as a list comprehension with multiple for constructs: data = [[1, 2], [3, 4], [5, 6]] output = [element for each_list in data for element in each_list] print(output) # Out: [1, 2, 3, 4, 5, 6]

Live Demo In both the expanded form and the list comprehension, the outer loop (first for statement) comes first. In addition to being more compact, the nested comprehension is also significantly faster. In [1]: data = [[1,2],[3,4],[5,6]] In [2]: def f(): ...: output=[] ...: for each_list in data: ...: for element in each_list: ...: output.append(element) ...: return output In [3]: timeit f() 1000000 loops, best of 3: 1.37 µs per loop In [4]: timeit [inner for outer in data for inner in outer] 1000000 loops, best of 3: 632 ns per loop

The overhead for the function call above is about 140ns. Inline ifs are nested similarly, and may occur in any position after the first for: data = [[1], [2, 3], [4, 5]] output = [element for each_list in data if len(each_list) == 2 for element in each_list if element != 5] print(output) # Out: [2, 3, 4]

Live Demo For the sake of readability, however, you should consider using traditional for-loops. This is especially true when nesting is more than 2 levels deep, and/or the logic of the comprehension is too complex. multiple nested loop list GoalKicker.com – Python® Notes for Professionals

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comprehension could be error prone or it gives unexpected result.

Section 21.6: Generator Expressions Generator expressions are very similar to list comprehensions. The main difference is that it does not create a full set of results at once; it creates a generator object which can then be iterated over. For instance, see the difference in the following code: # list comprehension [x**2 for x in range(10)] # Output: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Python 2.x Version

≥ 2.4

# generator comprehension (x**2 for x in xrange(10)) # Output:

These are two very different objects: the list comprehension returns a list object whereas the generator comprehension returns a generator. generator objects cannot be indexed and makes use of the next function to get items in order.

Note: We use xrange since it too creates a generator object. If we would use range, a list would be created. Also, xrange exists only in later version of python 2. In python 3, range just returns a generator. For more information,

see the Differences between range and xrange functions example.

Python 2.x Version

≥ 2.4

g = (x**2 for x in xrange(10)) print(g[0]) Traceback (most recent call last): File "", line 1, in TypeError: 'generator' object has no attribute '__getitem__'

g.next() g.next() g.next() ... g.next()

# 0 # 1 # 4

g.next()

# Throws StopIteration Exception

# 81

Traceback (most recent call last): File "", line 1, in StopIteration

Python 3.x Version

≥ 3.0

NOTE: The function g.next() should be substituted by next(g) and xrange with range since Iterator.next() and xrange() do not exist in Python 3.

Although both of these can be iterated in a similar way:

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for i in [x**2 for x in range(10)]: print(i) """ Out: 0 1 4 ... 81 """

Python 2.x Version

≥ 2.4

for i in (x**2 for x in xrange(10)): print(i) """ Out: 0 1 4 . . . 81 """

Use cases Generator expressions are lazily evaluated, which means that they generate and return each value only when the generator is iterated. This is often useful when iterating through large datasets, avoiding the need to create a duplicate of the dataset in memory: for square in (x**2 for x in range(1000000)): #do something

Another common use case is to avoid iterating over an entire iterable if doing so is not necessary. In this example, an item is retrieved from a remote API with each iteration of get_objects(). Thousands of objects may exist, must be retrieved one-by-one, and we only need to know if an object matching a pattern exists. By using a generator expression, when we encounter an object matching the pattern. def get_objects(): """Gets objects from an API one by one""" while True: yield get_next_item() def object_matches_pattern(obj): # perform potentially complex calculation return matches_pattern def right_item_exists(): items = (object_matched_pattern(each) for each in get_objects()) for item in items: if item.is_the_right_one:

return True return False

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Section 21.7: Set Comprehensions Set comprehension is similar to list and dictionary comprehension, but it produces a set, which is an unordered collection of unique elements. Python 2.x Version

≥ 2.7

# A set containing every value in range(5): {x for x in range(5)} # Out: {0, 1, 2, 3, 4} # A set of even numbers between 1 and 10: {x for x in range(1, 11) if x % 2 == 0} # Out: {2, 4, 6, 8, 10} # Unique alphabetic characters in a string of text: text = "When in the Course of human events it becomes necessary for one people..." {ch.lower() for ch in text if ch.isalpha()} # Out: set(['a', 'c', 'b', 'e', 'f', 'i', 'h', 'm', 'l', 'o', # 'n', 'p', 's', 'r', 'u', 't', 'w', 'v', 'y'])

Live Demo Keep in mind that sets are unordered. This means that the order of the results in the set may differ from the one presented in the above examples. Note: Set comprehension is available since python 2.7+, unlike list comprehensions, which were added in 2.0. In Python 2.2 to Python 2.6, the set() function can be used with a generator expression to produce the same result: Python 2.x Version

≥ 2.2

set(x for x in range(5)) # Out: {0, 1, 2, 3, 4}

Section 21.8: Refactoring filter and map to list comprehensions The filter or map functions should often be replaced by list comprehensions. Guido Van Rossum describes this well in an open letter in 2005: filter(P, S) is almost always written clearer as [x for x in S if P(x)], and this has the huge

advantage that the most common usages involve predicates that are comparisons, e.g. x==42, and defining a lambda for that just requires much more effort for the reader (plus the lambda is slower than the list comprehension). Even more so for map(F, S) which becomes [F(x) for x in S]. Of course, in many cases you'd be able to use generator expressions instead. The following lines of code are considered "not pythonic" and will raise errors in many python linters. filter(lambda x: x % 2 == 0, range(10)) # even numbers < 10 map(lambda x: 2*x, range(10)) # multiply each number by two reduce(lambda x,y: x+y, range(10)) # sum of all elements in list

Taking what we have learned from the previous quote, we can break down these filter and map expressions into their equivalent list comprehensions; also removing the lambda functions from each - making the code more readable in the process. GoalKicker.com – Python® Notes for Professionals

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# Filter: # P(x) = x % 2 == 0 # S = range(10) [x for x in range(10) if x % 2 == 0] # Map # F(x) = 2*x # S = range(10) [2*x for x in range(10)]

Readability becomes even more apparent when dealing with chaining functions. Where due to readability, the results of one map or filter function should be passed as a result to the next; with simple cases, these can be replaced with a single list comprehension. Further, we can easily tell from the list comprehension what the outcome of our process is, where there is more cognitive load when reasoning about the chained Map & Filter process. # Map & Filter filtered = filter(lambda x: x % 2 == 0, range(10)) results = map(lambda x: 2*x, filtered) # List comprehension results = [2*x for x in range(10) if x % 2 == 0]

Refactoring - Quick Reference Map map(F, S) == [F(x) for x in S]

Filter filter(P, S) == [x for x in S if P(x)]

where F and P are functions which respectively transform input values and return a bool

Section 21.9: Comprehensions involving tuples The for clause of a list comprehension can specify more than one variable: [x + y for x, y in [(1, 2), (3, 4), (5, 6)]] # Out: [3, 7, 11] [x + y for x, y in zip([1, 3, 5], [2, 4, 6])] # Out: [3, 7, 11]

This is just like regular for loops: for x, y in [(1,2), (3,4), (5,6)]: print(x+y) # 3 # 7 # 11

Note however, if the expression that begins the comprehension is a tuple then it must be parenthesized: [x, y for x, y in [(1, 2), (3, 4), (5, 6)]]

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# SyntaxError: invalid syntax [(x, y) for x, y in [(1, 2), (3, 4), (5, 6)]] # Out: [(1, 2), (3, 4), (5, 6)]

Section 21.10: Counting Occurrences Using Comprehension When we want to count the number of items in an iterable, that meet some condition, we can use comprehension to produce an idiomatic syntax: # Count the numbers in `range(1000)` that are even and contain the digit `9`: print (sum( 1 for x in range(1000) if x % 2 == 0 and '9' in str(x) )) # Out: 95

The basic concept can be summarized as: 1. Iterate over the elements in range(1000). 2. Concatenate all the needed if conditions. 3. Use 1 as expression to return a 1 for each item that meets the conditions. 4. Sum up all the 1s to determine number of items that meet the conditions. Note: Here we are not collecting the 1s in a list (note the absence of square brackets), but we are passing the ones directly to the sum function that is summing them up. This is called a generator expression, which is similar to a Comprehension.

Section 21.11: Changing Types in a List Quantitative data is often read in as strings that must be converted to numeric types before processing. The types of all list items can be converted with either a List Comprehension or the map() function. # Convert a list of strings to integers. items = ["1","2","3","4"] [int(item) for item in items] # Out: [1, 2, 3, 4] # Convert a list of strings to float. items = ["1","2","3","4"] map(float, items) # Out:[1.0, 2.0, 3.0, 4.0]

Section 21.12: Nested List Comprehensions Nested list comprehensions, unlike list comprehensions with nested loops, are List comprehensions within a list comprehension. The initial expression can be any arbitrary expression, including another list comprehension. #List Comprehension with nested loop [x + y for x in [1, 2, 3] for y in [3, 4, 5]] #Out: [4, 5, 6, 5, 6, 7, 6, 7, 8] #Nested List Comprehension [[x + y for x in [1, 2, 3]] for y in [3, 4, 5]] #Out: [[4, 5, 6], [5, 6, 7], [6, 7, 8]]

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The Nested example is equivalent to l = [] for y in [3, 4, 5]: temp = [] for x in [1, 2, 3]: temp.append(x + y) l.append(temp)

One example where a nested comprehension can be used it to transpose a matrix. matrix = [[1,2,3], [4,5,6], [7,8,9]] [[row[i] for row in matrix] for i in range(len(matrix))] # [[1, 4, 7], [2, 5, 8], [3, 6, 9]]

Like nested for loops, there is no limit to how deep comprehensions can be nested. [[[i + j + k for k in 'cd'] for j in 'ab'] for i in '12'] # Out: [[['1ac', '1ad'], ['1bc', '1bd']], [['2ac', '2ad'], ['2bc', '2bd']]]

Section 21.13: Iterate two or more list simultaneously within list comprehension For iterating more than two lists simultaneously within list comprehension, one may use zip() as: >>> list_1 = [1, 2, 3 , 4] >>> list_2 = ['a', 'b', 'c', 'd'] >>> list_3 = ['6', '7', '8', '9'] # Two lists >>> [(i, j) for i, j in zip(list_1, list_2)] [(1, 'a'), (2, 'b'), (3, 'c'), (4, 'd')] # Three lists >>> [(i, j, k) for i, j, k in zip(list_1, list_2, list_3)] [(1, 'a', '6'), (2, 'b', '7'), (3, 'c', '8'), (4, 'd', '9')] # so on ...

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Chapter 22: List slicing (selecting parts of lists) Section 22.1: Using the third "step" argument lst = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'] lst[::2] # Output: ['a', 'c', 'e', 'g'] lst[::3] # Output: ['a', 'd', 'g']

Section 22.2: Selecting a sublist from a list lst = ['a', 'b', 'c', 'd', 'e'] lst[2:4] # Output: ['c', 'd'] lst[2:] # Output: ['c', 'd', 'e'] lst[:4] # Output: ['a', 'b', 'c', 'd']

Section 22.3: Reversing a list with slicing a = [1, 2, 3, 4, 5] # steps through the list backwards (step=-1) b = a[::-1] # built-in list method to reverse 'a' a.reverse() if a = b: print(True) print(b) # Output: # True # [5, 4, 3, 2, 1]

Section 22.4: Shifting a list using slicing def shift_list(array, s): """Shifts the elements of a list to the left or right. Args: array - the list to shift s - the amount to shift the list ('+': right-shift, '-': left-shift) Returns: shifted_array - the shifted list

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""" # calculate actual shift amount (e.g., 11 --> 1 if length of the array is 5) s %= len(array) # reverse the shift direction to be more intuitive s *= -1 # shift array with list slicing shifted_array = array[s:] + array[:s] return shifted_array my_array = [1, 2, 3, 4, 5] # negative numbers shift_list(my_array, -7) >>> [3, 4, 5, 1, 2] # no shift on numbers equal to the size of the array shift_list(my_array, 5) >>> [1, 2, 3, 4, 5] # works on positive numbers shift_list(my_array, 3) >>> [3, 4, 5, 1, 2]

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Chapter 23: groupby() Parameter Details iterable Any python iterable key

Function(criteria) on which to group the iterable

In Python, the itertools.groupby() method allows developers to group values of an iterable class based on a specified property into another iterable set of values.

Section 23.1: Example 4 In this example we see what happens when we use different types of iterable. things = [("animal", "bear"), ("animal", "duck"), ("plant", "cactus"), ("vehicle", "harley"), \ ("vehicle", "speed boat"), ("vehicle", "school bus")] dic = {} f = lambda x: x[0] for key, group in groupby(sorted(things, key=f), f): dic[key] = list(group) dic

Results in {'animal': [('animal', 'bear'), ('animal', 'duck')], 'plant': [('plant', 'cactus')], 'vehicle': [('vehicle', 'harley'), ('vehicle', 'speed boat'), ('vehicle', 'school bus')]}

This example below is essentially the same as the one above it. The only difference is that I have changed all the tuples to lists. things = [["animal", "bear"], ["animal", "duck"], ["vehicle", "harley"], ["plant", "cactus"], \ ["vehicle", "speed boat"], ["vehicle", "school bus"]] dic = {} f = lambda x: x[0] for key, group in groupby(sorted(things, key=f), f): dic[key] = list(group) dic

Results {'animal': [['animal', 'bear'], ['animal', 'duck']], 'plant': [['plant', 'cactus']], 'vehicle': [['vehicle', 'harley'], ['vehicle', 'speed boat'], ['vehicle', 'school bus']]}

Section 23.2: Example 2 This example illustrates how the default key is chosen if we do not specify any c = groupby(['goat', 'dog', 'cow', 1, 1, 2, 3, 11, 10, ('persons', 'man', 'woman')]) dic = {} for k, v in c:

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dic[k] = list(v) dic

Results in {1: [1, 1], 2: [2], 3: [3], ('persons', 'man', 'woman'): [('persons', 'man', 'woman')], 'cow': ['cow'], 'dog': ['dog'], 10: [10], 11: [11], 'goat': ['goat']}

Notice here that the tuple as a whole counts as one key in this list

Section 23.3: Example 3 Notice in this example that mulato and camel don't show up in our result. Only the last element with the specified key shows up. The last result for c actually wipes out two previous results. But watch the new version where I have the data sorted first on same key. list_things = ['goat', 'dog', 'donkey', 'mulato', 'cow', 'cat', ('persons', 'man', 'woman'), \ 'wombat', 'mongoose', 'malloo', 'camel'] c = groupby(list_things, key=lambda x: x[0]) dic = {} for k, v in c: dic[k] = list(v) dic

Results in {'c': ['camel'], 'd': ['dog', 'donkey'], 'g': ['goat'], 'm': ['mongoose', 'malloo'], 'persons': [('persons', 'man', 'woman')], 'w': ['wombat']}

Sorted Version list_things = ['goat', 'dog', 'donkey', 'mulato', 'cow', 'cat', ('persons', 'man', 'woman'), \ 'wombat', 'mongoose', 'malloo', 'camel'] sorted_list = sorted(list_things, key = lambda x: x[0]) print(sorted_list) print() c = groupby(sorted_list, key=lambda x: x[0]) dic = {} for k, v in c: dic[k] = list(v) dic

Results in ['cow', 'cat', 'camel', 'dog', 'donkey', 'goat', 'mulato', 'mongoose', 'malloo', ('persons', 'man', 'woman'), 'wombat']

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{'c': ['cow', 'cat', 'camel'], 'd': ['dog', 'donkey'], 'g': ['goat'], 'm': ['mulato', 'mongoose', 'malloo'], 'persons': [('persons', 'man', 'woman')], 'w': ['wombat']}

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Chapter 24: Linked lists A linked list is a collection of nodes, each made up of a reference and a value. Nodes are strung together into a sequence using their references. Linked lists can be used to implement more complex data structures like lists, stacks, queues, and associative arrays.

Section 24.1: Single linked list example This example implements a linked list with many of the same methods as that of the built-in list object. class Node: def __init__(self, val): self.data = val self.next = None def getData(self): return self.data def getNext(self): return self.next def setData(self, val): self.data = val def setNext(self, val): self.next = val class LinkedList: def __init__(self): self.head = None def isEmpty(self): """Check if the list is empty""" return self.head is None def add(self, item): """Add the item to the list""" new_node = Node(item) new_node.setNext(self.head) self.head = new_node def size(self): """Return the length/size of the list""" count = 0 current = self.head while current is not None: count += 1 current = current.getNext() return count def search(self, item): """Search for item in list. If found, return True. If not found, return False""" current = self.head found = False while current is not None and not found: if current.getData() is item: found = True else: current = current.getNext()

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return found def remove(self, item): """Remove item from list. If item is not found in list, raise ValueError""" current = self.head previous = None found = False while current is not None and not found: if current.getData() is item: found = True else: previous = current current = current.getNext() if found: if previous is None: self.head = current.getNext() else: previous.setNext(current.getNext()) else: raise ValueError print 'Value not found.' def insert(self, position, item): """ Insert item at position specified. If position specified is out of bounds, raise IndexError """ if position > self.size() - 1: raise IndexError print "Index out of bounds." current = self.head previous = None pos = 0 if position is 0: self.add(item) else: new_node = Node(item) while pos < position: pos += 1 previous = current current = current.getNext() previous.setNext(new_node) new_node.setNext(current) def index(self, item): """ Return the index where item is found. If item is not found, return None. """ current = self.head pos = 0 found = False while current is not None and not found: if current.getData() is item: found = True else: current = current.getNext() pos += 1 if found: pass else: pos = None

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return pos def pop(self, position = None): """ If no argument is provided, return and remove the item at the head. If position is provided, return and remove the item at that position. If index is out of bounds, raise IndexError """ if position > self.size(): print 'Index out of bounds' raise IndexError current = self.head if position is None: ret = current.getData() self.head = current.getNext() else: pos = 0 previous = None while pos < position: previous = current current = current.getNext() pos += 1 ret = current.getData() previous.setNext(current.getNext()) print ret return ret def append(self, item): """Append item to the end of the list""" current = self.head previous = None pos = 0 length = self.size() while pos < length: previous = current current = current.getNext() pos += 1 new_node = Node(item) if previous is None: new_node.setNext(current) self.head = new_node else: previous.setNext(new_node) def printList(self): """Print the list""" current = self.head while current is not None: print current.getData() current = current.getNext()

Usage functions much like that of the built-in list. ll = LinkedList() ll.add('l') ll.add('H') ll.insert(1,'e') ll.append('l') ll.append('o') ll.printList()

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H e l l o

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Chapter 25: Linked List Node Section 25.1: Write a simple Linked List Node in python A linked list is either: the empty list, represented by None, or a node that contains a cargo object and a reference to a linked list. #! /usr/bin/env python class Node: def __init__(self, cargo=None, next=None): self.car = cargo self.cdr = next def __str__(self): return str(self.car)

def display(lst): if lst: w("%s " % lst) display(lst.cdr) else: w("nil\n")

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Chapter 26: Filter Parameter function iterable

Details callable that determines the condition or None then use the identity function for filtering (positionalonly) iterable that will be filtered (positional-only)

Section 26.1: Basic use of filter To filter discards elements of a sequence based on some criteria: names = ['Fred', 'Wilma', 'Barney'] def long_name(name): return len(name) > 5

Python 2.x Version

≥ 2.0

filter(long_name, names) # Out: ['Barney'] [name for name in names if len(name) > 5] # equivalent list comprehension # Out: ['Barney']

from itertools import ifilter ifilter(long_name, names) # as generator (similar to python 3.x filter builtin) # Out: list(ifilter(long_name, names)) # equivalent to filter with lists # Out: ['Barney'] (name for name in names if len(name) > 5) # equivalent generator expression # Out:

Python 2.x Version

≥ 2.6

# Besides the options for older python 2.x versions there is a future_builtin function: from future_builtins import filter filter(long_name, names) # identical to itertools.ifilter # Out:

Python 3.x Version

≥ 3.0

filter(long_name, names) # returns a generator # Out: list(filter(long_name, names)) # cast to list # Out: ['Barney'] (name for name in names if len(name) > 5) # equivalent generator expression # Out:

Section 26.2: Filter without function If the function parameter is None, then the identity function will be used: list(filter(None, [1, 0, 2, [], '', 'a'])) # Out: [1, 2, 'a']

Python 2.x Version

# discards 0, [] and ''

≥ 2.0.1

[i for i in [1, 0, 2, [], '', 'a'] if i] # equivalent list comprehension

Python 3.x Version

≥ 3.0.0

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(i for i in [1, 0, 2, [], '', 'a'] if i) # equivalent generator expression

Section 26.3: Filter as short-circuit check filter (python 3.x) and ifilter (python 2.x) return a generator so they can be very handy when creating a short-

circuit test like or or and: Python 2.x Version

≥ 2.0.1

# not recommended in real use but keeps the example short: from itertools import ifilter as filter

Python 2.x Version

≥ 2.6.1

from future_builtins import filter

To find the first element that is smaller than 100: car_shop = [('Toyota', 1000), ('rectangular tire', 80), ('Porsche', 5000)] def find_something_smaller_than(name_value_tuple): print('Check {0}, {1}$'.format(*name_value_tuple) return name_value_tuple[1] < 100 next(filter(find_something_smaller_than, car_shop)) # Print: Check Toyota, 1000$ # Check rectangular tire, 80$ # Out: ('rectangular tire', 80)

The next-function gives the next (in this case first) element of and is therefore the reason why it's short-circuit.

Section 26.4: Complementary function: filterfalse, ifilterfalse There is a complementary function for filter in the itertools-module: Python 2.x Version

≥ 2.0.1

# not recommended in real use but keeps the example valid for python 2.x and python 3.x from itertools import ifilterfalse as filterfalse

Python 3.x Version

≥ 3.0.0

from itertools import filterfalse

which works exactly like the generator filter but keeps only the elements that are False: # Usage without function (None): list(filterfalse(None, [1, 0, 2, [], '', 'a'])) # Out: [0, [], '']

# discards 1, 2, 'a'

# Usage with function names = ['Fred', 'Wilma', 'Barney'] def long_name(name): return len(name) > 5 list(filterfalse(long_name, names)) # Out: ['Fred', 'Wilma']

# Short-circuit usage with next: car_shop = [('Toyota', 1000), ('rectangular tire', 80), ('Porsche', 5000)] def find_something_smaller_than(name_value_tuple):

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print('Check {0}, {1}$'.format(*name_value_tuple) return name_value_tuple[1] < 100 next(filterfalse(find_something_smaller_than, car_shop)) # Print: Check Toyota, 1000$ # Out: ('Toyota', 1000)

# Using an equivalent generator: car_shop = [('Toyota', 1000), ('rectangular tire', 80), ('Porsche', 5000)] generator = (car for car in car_shop if not car[1] < 100) next(generator)

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Chapter 27: Heapq Section 27.1: Largest and smallest items in a collection To find the largest items in a collection, heapq module has a function called nlargest, we pass it two arguments, the first one is the number of items that we want to retrieve, the second one is the collection name: import heapq

numbers = [1, 4, 2, 100, 20, 50, 32, 200, 150, 8] print(heapq.nlargest(4, numbers)) # [200, 150, 100, 50]

Similarly, to find the smallest items in a collection, we use nsmallest function: print(heapq.nsmallest(4, numbers))

# [1, 2, 4, 8]

Both nlargest and nsmallest functions take an optional argument (key parameter) for complicated data structures. The following example shows the use of age property to retrieve the oldest and the youngest people from people dictionary: people = [ {'firstname': {'firstname': {'firstname': {'firstname': {'firstname': {'firstname': ]

'John', 'lastname': 'Doe', 'age': 30}, 'Jane', 'lastname': 'Doe', 'age': 25}, 'Janie', 'lastname': 'Doe', 'age': 10}, 'Jane', 'lastname': 'Roe', 'age': 22}, 'Johnny', 'lastname': 'Doe', 'age': 12}, 'John', 'lastname': 'Roe', 'age': 45}

oldest = heapq.nlargest(2, people, key=lambda s: s['age']) print(oldest) # Output: [{'firstname': 'John', 'age': 45, 'lastname': 'Roe'}, {'firstname': 'John', 'age': 30, 'lastname': 'Doe'}] youngest = heapq.nsmallest(2, people, key=lambda s: s['age']) print(youngest) # Output: [{'firstname': 'Janie', 'age': 10, 'lastname': 'Doe'}, {'firstname': 'Johnny', 'age': 12, 'lastname': 'Doe'}]

Section 27.2: Smallest item in a collection The most interesting property of a heap is that its smallest element is always the first element: heap[0] import heapq

numbers = [10, 4, 2, 100, 20, 50, 32, 200, 150, 8] heapq.heapify(numbers) print(numbers) # Output: [2, 4, 10, 100, 8, 50, 32, 200, 150, 20] heapq.heappop(numbers) # 2 print(numbers) # Output: [4, 8, 10, 100, 20, 50, 32, 200, 150]

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heapq.heappop(numbers) # 4 print(numbers) # Output: [8, 20, 10, 100, 150, 50, 32, 200]

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Chapter 28: Tuple A tuple is an immutable list of values. Tuples are one of Python's simplest and most common collection types, and can be created with the comma operator (value = 1, 2, 3).

Section 28.1: Tuple Syntactically, a tuple is a comma-separated list of values: t = 'a', 'b', 'c', 'd', 'e'

Although not necessary, it is common to enclose tuples in parentheses: t = ('a', 'b', 'c', 'd', 'e')

Create an empty tuple with parentheses: t0 = () type(t0)

#

To create a tuple with a single element, you have to include a final comma: t1 = 'a', type(t1)

#

Note that a single value in parentheses is not a tuple: t2 = ('a') type(t2)

#

To create a singleton tuple it is necessary to have a trailing comma. t2 = ('a',) type(t2)

#

Note that for singleton tuples it's recommended (see PEP8 on trailing commas) to use parentheses. Also, no white space after the trailing comma (see PEP8 on whitespaces) t2 = ('a',) t2 = 'a', t2 = ('a', )

# PEP8-compliant # this notation is not recommended by PEP8 # this notation is not recommended by PEP8

Another way to create a tuple is the built-in function tuple. t = tuple('lupins') print(t) t = tuple(range(3)) print(t)

# ('l', 'u', 'p', 'i', 'n', 's') # (0, 1, 2)

These examples are based on material from the book Think Python by Allen B. Downey.

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Section 28.2: Tuples are immutable One of the main differences between lists and tuples in Python is that tuples are immutable, that is, one cannot add or modify items once the tuple is initialized. For example: >>> t = (1, 4, 9) >>> t[0] = 2 Traceback (most recent call last): File "", line 1, in TypeError: 'tuple' object does not support item assignment

Similarly, tuples don't have .append and .extend methods as list does. Using += is possible, but it changes the binding of the variable, and not the tuple itself: >>> >>> >>> >>> (1, >>> (1,

t = (1, 2) q = t t += (3, 4) t 2, 3, 4) q 2)

Be careful when placing mutable objects, such as lists, inside tuples. This may lead to very confusing outcomes when changing them. For example: >>> t = (1, 2, 3, [1, 2, 3]) (1, 2, 3, [1, 2, 3]) >>> t[3] += [4, 5]

Will both raise an error and change the contents of the list within the tuple: TypeError: 'tuple' object does not support item assignment >>> t (1, 2, 3, [1, 2, 3, 4, 5])

You can use the += operator to "append" to a tuple - this works by creating a new tuple with the new element you "appended" and assign it to its current variable; the old tuple is not changed, but replaced! This avoids converting to and from a list, but this is slow and is a bad practice, especially if you're going to append multiple times.

Section 28.3: Packing and Unpacking Tuples Tuples in Python are values separated by commas. Enclosing parentheses for inputting tuples are optional, so the two assignments a = 1, 2, 3

# a is the tuple (1, 2, 3)

and a = (1, 2, 3) # a is the tuple (1, 2, 3)

are equivalent. The assignment a = 1, 2, 3 is also called packing because it packs values together in a tuple. Note that a one-value tuple is also a tuple. To tell Python that a variable is a tuple and not a single value you can use GoalKicker.com – Python® Notes for Professionals

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a trailing comma a = 1 # a is the value 1 a = 1, # a is the tuple (1,)

A comma is needed also if you use parentheses a = (1,) # a is the tuple (1,) a = (1) # a is the value 1 and not a tuple

To unpack values from a tuple and do multiple assignments use # unpacking AKA multiple assignment x, y, z = (1, 2, 3) # x == 1 # y == 2 # z == 3

The symbol _ can be used as a disposable variable name if one only needs some elements of a tuple, acting as a placeholder: a = 1, 2, 3, 4 _, x, y, _ = a # x == 2 # y == 3

Single element tuples: x, = 1, x = 1,

# x is the value 1 # x is the tuple (1,)

In Python 3 a target variable with a * prefix can be used as a catch-all variable (see Unpacking Iterables ): Python 3.x Version

≥ 3.0

first, *more, last = (1, 2, 3, 4, 5) # first == 1 # more == [2, 3, 4] # last == 5

Section 28.4: Built-in Tuple Functions Tuples support the following build-in functions Comparison If elements are of the same type, python performs the comparison and returns the result. If elements are different types, it checks whether they are numbers. If numbers, perform comparison. If either element is a number, then the other element is returned. Otherwise, types are sorted alphabetically . If we reached the end of one of the lists, the longer list is "larger." If both list are same it returns 0. tuple1 = ('a', 'b', 'c', 'd', 'e') tuple2 = ('1','2','3')

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tuple3 = ('a', 'b', 'c', 'd', 'e') cmp(tuple1, tuple2) Out: 1 cmp(tuple2, tuple1) Out: -1 cmp(tuple1, tuple3) Out: 0

Tuple Length The function len returns the total length of the tuple len(tuple1) Out: 5

Max of a tuple The function max returns item from the tuple with the max value max(tuple1) Out: 'e' max(tuple2) Out: '3'

Min of a tuple The function min returns the item from the tuple with the min value min(tuple1) Out: 'a' min(tuple2) Out: '1'

Convert a list into tuple The built-in function tuple converts a list into a tuple. list = [1,2,3,4,5] tuple(list) Out: (1, 2, 3, 4, 5)

Tuple concatenation Use + to concatenate two tuples tuple1 + tuple2 Out: ('a', 'b', 'c', 'd', 'e', '1', '2', '3')

Section 28.5: Tuple Are Element-wise Hashable and Equatable hash( (1, 2) ) # ok hash( ([], {"hello"})

# not ok, since lists and sets are not hashabe

Thus a tuple can be put inside a set or as a key in a dict only if each of its elements can. { (1, 2) } #

ok

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{ ([], {"hello"}) ) # not ok

Section 28.6: Indexing Tuples x = (1, x[0] # x[1] # x[2] # x[3] #

2, 3) 1 2 3 IndexError: tuple index out of range

Indexing with negative numbers will start from the last element as -1: x[-1] x[-2] x[-3] x[-4]

# # # #

3 2 1 IndexError: tuple index out of range

Indexing a range of elements print(x[:-1]) print(x[-1:]) print(x[1:3])

# (1, 2) # (3,) # (2, 3)

Section 28.7: Reversing Elements Reverse elements within a tuple colors = "red", "green", "blue" rev = colors[::-1] # rev: ("blue", "green", "red") colors = rev # colors: ("blue", "green", "red")

Or using reversed (reversed gives an iterable which is converted to a tuple): rev = tuple(reversed(colors)) # rev: ("blue", "green", "red") colors = rev # colors: ("blue", "green", "red")

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Chapter 29: Basic Input and Output Section 29.1: Using the print function Python 3.x Version

≥ 3.0

In Python 3, print functionality is in the form of a function: print("This string will be displayed in the output") # This string will be displayed in the output print("You can print \n escape characters too.") # You can print escape characters too.

Python 2.x Version

≥ 2.3

In Python 2, print was originally a statement, as shown below. print "This string will be displayed in the output" # This string will be displayed in the output print "You can print \n escape characters too." # You can print escape characters too.

Note: using from __future__ import print_function in Python 2 will allow users to use the print() function the same as Python 3 code. This is only available in Python 2.6 and above.

Section 29.2: Input from a File Input can also be read from files. Files can be opened using the built-in function open. Using a with as syntax (called a 'Context Manager') makes using open and getting a handle for the file super easy: with open('somefile.txt', 'r') as fileobj: # write code here using fileobj

This ensures that when code execution leaves the block the file is automatically closed. Files can be opened in different modes. In the above example the file is opened as read-only. To open an existing file for reading only use r. If you want to read that file as bytes use rb. To append data to an existing file use a. Use w to create a file or overwrite any existing files of the same name. You can use r+ to open a file for both reading and

writing. The first argument of open() is the filename, the second is the mode. If mode is left blank, it will default to r. # let's create an example file: with open('shoppinglist.txt', 'w') as fileobj: fileobj.write('tomato\npasta\ngarlic') with open('shoppinglist.txt', 'r') as fileobj: # this method makes a list where each line # of the file is an element in the list lines = fileobj.readlines() print(lines) # ['tomato\n', 'pasta\n', 'garlic'] with open('shoppinglist.txt', 'r') as fileobj:

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# here we read the whole content into one string: content = fileobj.read() # get a list of lines, just like int the previous example: lines = content.split('\n') print(lines) # ['tomato', 'pasta', 'garlic']

If the size of the file is tiny, it is safe to read the whole file contents into memory. If the file is very large it is often better to read line-by-line or by chunks, and process the input in the same loop. To do that: with open('shoppinglist.txt', 'r') as fileobj: # this method reads line by line: lines = [] for line in fileobj: lines.append(line.strip())

When reading files, be aware of the operating system-specific line-break characters. Although for line in fileobj automatically strips them off, it is always safe to call strip() on the lines read, as it is shown above.

Opened files (fileobj in the above examples) always point to a specific location in the file. When they are first opened the file handle points to the very beginning of the file, which is the position 0. The file handle can display its current position with tell: fileobj = open('shoppinglist.txt', 'r') pos = fileobj.tell() print('We are at %u.' % pos) # We are at 0.

Upon reading all the content, the file handler's position will be pointed at the end of the file: content = fileobj.read() end = fileobj.tell() print('This file was %u characters long.' % end) # This file was 22 characters long. fileobj.close()

The file handler position can be set to whatever is needed: fileobj = open('shoppinglist.txt', 'r') fileobj.seek(7) pos = fileobj.tell() print('We are at character #%u.' % pos)

You can also read any length from the file content during a given call. To do this pass an argument for read(). When read() is called with no argument it will read until the end of the file. If you pass an argument it will read that number of bytes or characters, depending on the mode (rb and r respectively): # reads the next 4 characters # starting at the current position next4 = fileobj.read(4) # what we got? print(next4) # 'cucu' # where we are now? pos = fileobj.tell() print('We are at %u.' % pos) # We are at 11, as we was at 7, and read 4 chars. fileobj.close()

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To demonstrate the difference between characters and bytes: with open('shoppinglist.txt', 'r') as fileobj: print(type(fileobj.read())) # with open('shoppinglist.txt', 'rb') as fileobj: print(type(fileobj.read())) #

Section 29.3: Read from stdin Python programs can read from unix pipelines. Here is a simple example how to read from stdin: import sys for line in sys.stdin: print(line)

Be aware that sys.stdin is a stream. It means that the for-loop will only terminate when the stream has ended. You can now pipe the output of another program into your python program as follows: $ cat myfile | python myprogram.py

In this example cat myfile can be any unix command that outputs to stdout. Alternatively, using the fileinput module can come in handy: import fileinput for line in fileinput.input(): process(line)

Section 29.4: Using input() and raw_input() Python 2.x Version

≥ 2.3

raw_input will wait for the user to enter text and then return the result as a string. foo = raw_input("Put a message here that asks the user for input")

In the above example foo will store whatever input the user provides. Python 3.x Version

≥ 3.0

input will wait for the user to enter text and then return the result as a string. foo = input("Put a message here that asks the user for input")

In the above example foo will store whatever input the user provides.

Section 29.5: Function to prompt user for a number def input_number(msg, err_msg=None): while True: try:

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return float(raw_input(msg)) except ValueError: if err_msg is not None: print(err_msg)

def input_number(msg, err_msg=None): while True: try: return float(input(msg)) except ValueError: if err_msg is not None: print(err_msg)

And to use it: user_number = input_number("input a number: ", "that's not a number!")

Or, if you do not want an "error message": user_number = input_number("input a number: ")

Section 29.6: Printing a string without a newline at the end Python 2.x Version

≥ 2.3

In Python 2.x, to continue a line with print, end the print statement with a comma. It will automatically add a space. print "Hello,", print "World!" # Hello, World!

Python 3.x Version

≥ 3.0

In Python 3.x, the print function has an optional end parameter that is what it prints at the end of the given string. By default it's a newline character, so equivalent to this: print("Hello, ", end="\n") print("World!") # Hello, # World!

But you could pass in other strings print("Hello, ", end="") print("World!") # Hello, World! print("Hello, ", end="
") print("World!") # Hello,
World! print("Hello, ", end="BREAK") print("World!") # Hello, BREAKWorld!

If you want more control over the output, you can use sys.stdout.write: GoalKicker.com – Python® Notes for Professionals

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import sys sys.stdout.write("Hello, ") sys.stdout.write("World!") # Hello, World!

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Chapter 30: Files & Folders I/O Parameter Details filename the path to your file or, if the file is in the working directory, the filename of your file access_mode a string value that determines how the file is opened buffering

an integer value used for optional line buffering

When it comes to storing, reading, or communicating data, working with the files of an operating system is both necessary and easy with Python. Unlike other languages where file input and output requires complex reading and writing objects, Python simplifies the process only needing commands to open, read/write and close the file. This topic explains how Python can interface with files on the operating system.

Section 30.1: File modes There are different modes you can open a file with, specified by the mode parameter. These include: 'r' - reading mode. The default. It allows you only to read the file, not to modify it. When using this mode the

file must exist. 'w' - writing mode. It will create a new file if it does not exist, otherwise will erase the file and allow you to

write to it. 'a' - append mode. It will write data to the end of the file. It does not erase the file, and the file must exist for

this mode. 'rb' - reading mode in binary. This is similar to r except that the reading is forced in binary mode. This is

also a default choice. 'r+' - reading mode plus writing mode at the same time. This allows you to read and write into files at the

same time without having to use r and w. 'rb+' - reading and writing mode in binary. The same as r+ except the data is in binary 'wb' - writing mode in binary. The same as w except the data is in binary. 'w+' - writing and reading mode. The exact same as r+ but if the file does not exist, a new one is made.

Otherwise, the file is overwritten. 'wb+' - writing and reading mode in binary mode. The same as w+ but the data is in binary. 'ab' - appending in binary mode. Similar to a except that the data is in binary. 'a+' - appending and reading mode. Similar to w+ as it will create a new file if the file does not exist.

Otherwise, the file pointer is at the end of the file if it exists. 'ab+' - appending and reading mode in binary. The same as a+ except that the data is in binary. with open(filename, 'r') as f: f.read() with open(filename, 'w') as f: f.write(filedata) with open(filename, 'a') as f: f.write('\\n' + newdata)

r Read

✔

r+ w w+ a a+ ✔ ✘ ✔ ✘ ✔

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Write

✘

✔

✔

✔

✔

✔

Creates file

✘

✘

✔

✔

✔

✔

Erases file

✘

✘

✔

✔

✘

✘

Initial position Start Start Start Start End End Python 3 added a new mode for exclusive creation so that you will not accidentally truncate or overwrite and existing file. 'x' - open for exclusive creation, will raise FileExistsError if the file already exists 'xb' - open for exclusive creation writing mode in binary. The same as x except the data is in binary. 'x+' - reading and writing mode. Similar to w+ as it will create a new file if the file does not exist. Otherwise,

will raise FileExistsError. 'xb+' - writing and reading mode. The exact same as x+ but the data is binary

x Read

✘

x+ ✔

Write

✔

✔

Creates file

✔

✔

Erases file

✘

✘

Initial position Start Start Allow one to write your file open code in a more pythonic manner: Python 3.x Version

≥ 3.3

try: with open("fname", "r") as fout: # Work with your open file except FileExistsError: # Your error handling goes here

In Python 2 you would have done something like Python 2.x Version

≥ 2.0

import os.path if os.path.isfile(fname): with open("fname", "w") as fout: # Work with your open file else: # Your error handling goes here

Section 30.2: Reading a file line-by-line The simplest way to iterate over a file line-by-line: with open('myfile.txt', 'r') as fp: for line in fp: print(line) readline() allows for more granular control over line-by-line iteration. The example below is equivalent to the one

above: with open('myfile.txt', 'r') as fp: while True: cur_line = fp.readline()

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# If the result is an empty string if cur_line == '': # We have reached the end of the file break print(cur_line)

Using the for loop iterator and readline() together is considered bad practice. More commonly, the readlines() method is used to store an iterable collection of the file's lines: with open("myfile.txt", "r") as fp: lines = fp.readlines() for i in range(len(lines)): print("Line " + str(i) + ": " + line)

This would print the following: Line 0: hello Line 1: world

Section 30.3: Iterate files (recursively) To iterate all files, including in sub directories, use os.walk: import os for root, folders, files in os.walk(root_dir): for filename in files: print root, filename

root_dir can be "." to start from current directory, or any other path to start from. Python 3.x Version

≥ 3.5

If you also wish to get information about the file, you may use the more efficient method os.scandir like so: for entry in os.scandir(path): if not entry.name.startswith('.') and entry.is_file(): print(entry.name)

Section 30.4: Getting the full contents of a file The preferred method of file i/o is to use the with keyword. This will ensure the file handle is closed once the reading or writing has been completed. with open('myfile.txt') as in_file: content = in_file.read() print(content)

or, to handle closing the file manually, you can forgo with and simply call close yourself: in_file = open('myfile.txt', 'r') content = in_file.read() print(content)

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in_file.close()

Keep in mind that without using a with statement, you might accidentally keep the file open in case an unexpected exception arises like so: in_file = open('myfile.txt', 'r') raise Exception("oops") in_file.close() # This will never be called

Section 30.5: Writing to a file with open('myfile.txt', 'w') as f: f.write("Line 1") f.write("Line 2") f.write("Line 3") f.write("Line 4")

If you open myfile.txt, you will see that its contents are: Line 1Line 2Line 3Line 4 Python doesn't automatically add line breaks, you need to do that manually: with open('myfile.txt', 'w') as f: f.write("Line 1\n") f.write("Line 2\n") f.write("Line 3\n") f.write("Line 4\n")

Line 1 Line 2 Line 3 Line 4 Do not use os.linesep as a line terminator when writing files opened in text mode (the default); use \n instead. If you want to specify an encoding, you simply add the encoding parameter to the open function: with open('my_file.txt', 'w', encoding='utf-8') as f: f.write('utf-8 text')

It is also possible to use the print statement to write to a file. The mechanics are different in Python 2 vs Python 3, but the concept is the same in that you can take the output that would have gone to the screen and send it to a file instead. Python 3.x Version

≥ 3.0

with open('fred.txt', 'w') as outfile: s = "I'm Not Dead Yet!" print(s) # writes to stdout print(s, file = outfile) # writes to outfile #Note: it is possible to specify the file parameter AND write to the screen #by making sure file ends up with a None value either directly or via a variable

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myfile = None print(s, file = myfile) # writes to stdout print(s, file = None) # writes to stdout

In Python 2 you would have done something like Python 2.x Version

≥ 2.0

outfile = open('fred.txt', 'w') s = "I'm Not Dead Yet!" print s # writes to stdout print >> outfile, s # writes to outfile

Unlike using the write function, the print function does automatically add line breaks.

Section 30.6: Check whether a file or path exists Employ the EAFP coding style and try to open it. import errno try: with open(path) as f: # File exists except IOError as e: # Raise the exception if it is not ENOENT (No such file or directory) if e.errno != errno.ENOENT: raise # No such file or directory

This will also avoid race-conditions if another process deleted the file between the check and when it is used. This race condition could happen in the following cases: Using the os module: import os os.path.isfile('/path/to/some/file.txt')

Python 3.x Version

≥ 3.4

Using pathlib: import pathlib path = pathlib.Path('/path/to/some/file.txt') if path.is_file(): ...

To check whether a given path exists or not, you can follow the above EAFP procedure, or explicitly check the path: import os path = "/home/myFiles/directory1" if os.path.exists(path): ## Do stuff

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Section 30.7: Random File Access Using mmap Using the mmap module allows the user to randomly access locations in a file by mapping the file into memory. This is an alternative to using normal file operations. import mmap with open('filename.ext', 'r') as fd: # 0: map the whole file mm = mmap.mmap(fd.fileno(), 0) # print characters at indices 5 through 10 print mm[5:10] # print the line starting from mm's current position print mm.readline() # write a character to the 5th index mm[5] = 'a' # return mm's position to the beginning of the file mm.seek(0) # close the mmap object mm.close()

Section 30.8: Replacing text in a file import fileinput replacements = {'Search1': 'Replace1', 'Search2': 'Replace2'} for line in fileinput.input('filename.txt', inplace=True): for search_for in replacements: replace_with = replacements[search_for] line = line.replace(search_for, replace_with) print(line, end='')

Section 30.9: Checking if a file is empty >>> import os >>> os.stat(path_to_file).st_size == 0

or >>> import os >>> os.path.getsize(path_to_file) > 0

However, both will throw an exception if the file does not exist. To avoid having to catch such an error, do this: import os def is_empty_file(fpath): return os.path.isfile(fpath) and os.path.getsize(fpath) > 0

which will return a bool value.

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Section 30.10: Read a file between a range of lines So let's suppose you want to iterate only between some specific lines of a file You can make use of itertools for that import itertools with open('myfile.txt', 'r') as f: for line in itertools.islice(f, 12, 30): # do something here

This will read through the lines 13 to 20 as in python indexing starts from 0. So line number 1 is indexed as 0 As can also read some extra lines by making use of the next() keyword here. And when you are using the file object as an iterable, please don't use the readline() statement here as the two techniques of traversing a file are not to be mixed together

Section 30.11: Copy a directory tree import shutil source='//192.168.1.2/Daily Reports' destination='D:\\Reports\\Today' shutil.copytree(source, destination)

The destination directory must not exist already.

Section 30.12: Copying contents of one file to a dierent file with open(input_file, 'r') as in_file, open(output_file, 'w') as out_file: for line in in_file: out_file.write(line)

Using the shutil module: import shutil shutil.copyfile(src, dst)

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Chapter 31: os.path This module implements some useful functions on pathnames. The path parameters can be passed as either strings, or bytes. Applications are encouraged to represent file names as (Unicode) character strings.

Section 31.1: Join Paths To join two or more path components together, firstly import os module of python and then use following: import os os.path.join('a', 'b', 'c')

The advantage of using os.path is that it allows code to remain compatible over all operating systems, as this uses the separator appropriate for the platform it's running on. For example, the result of this command on Windows will be: >>> os.path.join('a', 'b', 'c') 'a\b\c'

In an Unix OS: >>> os.path.join('a', 'b', 'c') 'a/b/c'

Section 31.2: Path Component Manipulation To split one component off of the path: >>> p = os.path.join(os.getcwd(), 'foo.txt') >>> p '/Users/csaftoiu/tmp/foo.txt' >>> os.path.dirname(p) '/Users/csaftoiu/tmp' >>> os.path.basename(p) 'foo.txt' >>> os.path.split(os.getcwd()) ('/Users/csaftoiu/tmp', 'foo.txt') >>> os.path.splitext(os.path.basename(p)) ('foo', '.txt')

Section 31.3: Get the parent directory os.path.abspath(os.path.join(PATH_TO_GET_THE_PARENT, os.pardir))

Section 31.4: If the given path exists to check if the given path exists path = '/home/john/temp' os.path.exists(path) #this returns false if path doesn't exist or if the path is a broken symbolic link

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Section 31.5: check if the given path is a directory, file, symbolic link, mount point etc to check if the given path is a directory dirname = '/home/john/python' os.path.isdir(dirname)

to check if the given path is a file filename = dirname + 'main.py' os.path.isfile(filename)

to check if the given path is symbolic link symlink = dirname + 'some_sym_link' os.path.islink(symlink)

to check if the given path is a mount point mount_path = '/home' os.path.ismount(mount_path)

Section 31.6: Absolute Path from Relative Path Use os.path.abspath: >>> os.getcwd() '/Users/csaftoiu/tmp' >>> os.path.abspath('foo') '/Users/csaftoiu/tmp/foo' >>> os.path.abspath('../foo') '/Users/csaftoiu/foo' >>> os.path.abspath('/foo') '/foo'

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Chapter 32: Iterables and Iterators Section 32.1: Iterator vs Iterable vs Generator An iterable is an object that can return an iterator. Any object with state that has an __iter__ method and returns an iterator is an iterable. It may also be an object without state that implements a __getitem__ method. - The method can take indices (starting from zero) and raise an IndexError when the indices are no longer valid. Python's str class is an example of a __getitem__ iterable. An Iterator is an object that produces the next value in a sequence when you call next(*object*) on some object. Moreover, any object with a __next__ method is an iterator. An iterator raises StopIteration after exhausting the iterator and cannot be re-used at this point. Iterable classes: Iterable classes define an __iter__ and a __next__ method. Example of an iterable class: class MyIterable: def __iter__(self): return self def __next__(self): #code #Classic iterable object in older versions of python, __getitem__ is still supported... class MySequence: def __getitem__(self, index): if (condition): raise IndexError return (item) #Can produce a plain `iterator` instance by using iter(MySequence())

Trying to instantiate the abstract class from the collections module to better see this. Example: Python 2.x Version

≥ 2.3

import collections >>> collections.Iterator() >>> TypeError: Cant instantiate abstract class Iterator with abstract methods next

Python 3.x Version

≥ 3.0

>>> TypeError: Cant instantiate abstract class Iterator with abstract methods __next__

Handle Python 3 compatibility for iterable classes in Python 2 by doing the following: Python 2.x Version

≥ 2.3

class MyIterable(object): #or collections.Iterator, which I'd recommend.... ....

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def __iter__(self): return self def next(self): #code __next__ = next

Both of these are now iterators and can be looped through: ex1 = MyIterableClass() ex2 = MySequence() for (item) in (ex1): #code for (item) in (ex2): #code

Generators are simple ways to create iterators. A generator is an iterator and an iterator is an iterable.

Section 32.2: Extract values one by one Start with iter() built-in to get iterator over iterable and use next() to get elements one by one until StopIteration is raised signifying the end: s i a b c

= = = = =

{1, 2} iter(s) next(i) next(i) next(i)

# # # # #

or list or generator or even iterator get iterator a = 1 b = 2 raises StopIteration

Section 32.3: Iterating over entire iterable s = {1, 2, 3} # get every element in s for a in s: print a # prints 1, then 2, then 3 # copy into list l1 = list(s) # l1 = [1, 2, 3] # use list comprehension l2 = [a * 2 for a in s if a > 2]

# l2 = [6]

Section 32.4: Verify only one element in iterable Use unpacking to extract the first element and ensure it's the only one: a, = iterable def foo(): yield 1 a, = foo()

# a = 1

nums = [1, 2, 3] a, = nums # ValueError: too many values to unpack

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Section 32.5: What can be iterable Iterable can be anything for which items are received one by one, forward only. Built-in Python collections are iterable: [1, (1, {1, {1:

2, 2, 2, 2,

3] 3) 3} 3: 4}

# # # #

list, iterate over items tuple set dict, iterate over keys

Generators return iterables: def foo(): # foo isn't iterable yet... yield 1 res = foo()

# ...but res already is

Section 32.6: Iterator isn't reentrant! def gen(): yield 1 iterable = gen() for a in iterable: print a # What was the first item of iterable? No way to get it now. # Only to get a new iterator gen()

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Chapter 33: Functions Parameter Details arg1, ..., argN Regular arguments *args

Unnamed positional arguments

kw1, ..., kwN Keyword-only arguments **kwargs

The rest of keyword arguments

Functions in Python provide organized, reusable and modular code to perform a set of specific actions. Functions simplify the coding process, prevent redundant logic, and make the code easier to follow. This topic describes the declaration and utilization of functions in Python. Python has many built-in functions like print(), input(), len(). Besides built-ins you can also create your own functions to do more specific jobs—these are called user-defined functions.

Section 33.1: Defining and calling simple functions Using the def statement is the most common way to define a function in python. This statement is a so called single clause compound statement with the following syntax: def function_name(parameters): statement(s) function_name is known as the identifier of the function. Since a function definition is an executable statement its

execution binds the function name to the function object which can be called later on using the identifier. parameters is an optional list of identifiers that get bound to the values supplied as arguments when the function is

called. A function may have an arbitrary number of arguments which are separated by commas. statement(s) – also known as the function body – are a nonempty sequence of statements executed each time the

function is called. This means a function body cannot be empty, just like any indented block. Here’s an example of a simple function definition which purpose is to print Hello each time it’s called: def greet(): print("Hello")

Now let’s call the defined greet() function: greet() # Out: Hello

That’s another example of a function definition which takes one single argument and displays the passed in value each time the function is called: def greet_two(greeting): print(greeting)

After that the greet_two() function must be called with an argument: greet_two("Howdy") # Out: Howdy

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Also you can give a default value to that function argument: def greet_two(greeting="Howdy"): print(greeting)

Now you can call the function without giving a value: greet_two() # Out: Howdy

You'll notice that unlike many other languages, you do not need to explicitly declare a return type of the function. Python functions can return values of any type via the return keyword. One function can return any number of different types! def many_types(x): if x < 0: return "Hello!" else: return 0 print(many_types(1)) print(many_types(-1)) # Output: 0 Hello!

As long as this is handled correctly by the caller, this is perfectly valid Python code. A function that reaches the end of execution without a return statement will always return None: def do_nothing(): pass print(do_nothing()) # Out: None

As mentioned previously a function definition must have a function body, a nonempty sequence of statements. Therefore the pass statement is used as function body, which is a null operation – when it is executed, nothing happens. It does what it means, it skips. It is useful as a placeholder when a statement is required syntactically, but no code needs to be executed.

Section 33.2: Defining a function with an arbitrary number of arguments Arbitrary number of positional arguments: Defining a function capable of taking an arbitrary number of arguments can be done by prefixing one of the arguments with a * def func(*args): # args will be a tuple containing all values that are passed in for i in args: print(i) func(1, 2, 3)

# Calling it with 3 arguments

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# Out: 1 # 2 # 3 list_of_arg_values = [1, 2, 3] func(*list_of_arg_values) # Calling it with list of values, * expands the list # Out: 1 # 2 # 3 func() # Calling it without arguments # No Output

You can't provide a default for args, for example func(*args=[1, 2, 3]) will raise a syntax error (won't even compile). You can't provide these by name when calling the function, for example func(*args=[1, 2, 3]) will raise a TypeError.

But if you already have your arguments in an array (or any other Iterable), you can invoke your function like this: func(*my_stuff).

These arguments (*args) can be accessed by index, for example args[0] will return the first argument Arbitrary number of keyword arguments You can take an arbitrary number of arguments with a name by defining an argument in the definition with two * in front of it: def func(**kwargs): # kwargs will be a dictionary containing the names as keys and the values as values for name, value in kwargs.items(): print(name, value) func(value1=1, value2=2, value3=3) # Out: value1 1 # value2 2 # value3 3

# Calling it with 3 arguments

func() # No Out put

# Calling it without arguments

my_dict = {'foo': 1, 'bar': 2} func(**my_dict) # Out: foo 1 # bar 2

# Calling it with a dictionary

You can't provide these without names, for example func(1, 2, 3) will raise a TypeError. kwargs is a plain native python dictionary. For example, args['value1'] will give the value for argument value1. Be

sure to check beforehand that there is such an argument or a KeyError will be raised. Warning You can mix these with other optional and required arguments but the order inside the definition matters. The positional/keyword arguments come first. (Required arguments). Then comes the arbitrary *arg arguments. (Optional). GoalKicker.com – Python® Notes for Professionals

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Then keyword-only arguments come next. (Required). Finally the arbitrary keyword **kwargs come. (Optional). # |-positional-|-optional-|---keyword-only--|-optional-| def func(arg1, arg2=10 , *args, kwarg1, kwarg2=2, **kwargs): pass arg1 must be given, otherwise a TypeError is raised. It can be given as positional (func(10)) or keyword

argument (func(arg1=10)). kwarg1 must also be given, but it can only be provided as keyword-argument: func(kwarg1=10). arg2 and kwarg2 are optional. If the value is to be changed the same rules as for arg1 (either positional or

keyword) and kwarg1 (only keyword) apply. *args catches additional positional parameters. But note, that arg1 and arg2 must be provided as positional

arguments to pass arguments to *args: func(1, 1, 1, 1). **kwargs catches all additional keyword parameters. In this case any parameter that is not arg1, arg2, kwarg1 or kwarg2. For example: func(kwarg3=10).

In Python 3, you can use * alone to indicate that all subsequent arguments must be specified as keywords. For instance the math.isclose function in Python 3.5 and higher is defined using def math.isclose (a, b, *, rel_tol=1e-09, abs_tol=0.0), which means the first two arguments can be supplied positionally but the

optional third and fourth parameters can only be supplied as keyword arguments. Python 2.x doesn't support keyword-only parameters. This behavior can be emulated with kwargs: def func(arg1, arg2=10, **kwargs): try: kwarg1 = kwargs.pop("kwarg1") except KeyError: raise TypeError("missing required keyword-only argument: 'kwarg1'") kwarg2 = kwargs.pop("kwarg2", 2) # function body ...

Note on Naming The convention of naming optional positional arguments args and optional keyword arguments kwargs is just a convention you can use any names you like but it is useful to follow the convention so that others know what you are doing, or even yourself later so please do. Note on Uniqueness Any function can be defined with none or one *args and none or one **kwargs but not with more than one of each. Also *args must be the last positional argument and **kwargs must be the last parameter. Attempting to use more than one of either will result in a Syntax Error exception. Note on Nesting Functions with Optional Arguments It is possible to nest such functions and the usual convention is to remove the items that the code has already handled but if you are passing down the parameters you need to pass optional positional args with a * prefix and optional keyword args with a ** prefix, otherwise args with be passed as a list or tuple and kwargs as a single dictionary. e.g.: def fn(**kwargs): print(kwargs) f1(**kwargs)

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def f1(**kwargs): print(len(kwargs)) fn(a=1, b=2) # Out: # {'a': 1, 'b': 2} # 2

Section 33.3: Lambda (Inline/Anonymous) Functions The lambda keyword creates an inline function that contains a single expression. The value of this expression is what the function returns when invoked. Consider the function: def greeting(): return "Hello"

which, when called as: print(greeting())

prints: Hello

This can be written as a lambda function as follows: greet_me = lambda: "Hello"

See note at the bottom of this section regarding the assignment of lambdas to variables. Generally, don't do it. This creates an inline function with the name greet_me that returns Hello. Note that you don't write return when creating a function with lambda. The value after : is automatically returned. Once assigned to a variable, it can be used just like a regular function: print(greet_me())

prints: Hello lambdas can take arguments, too: strip_and_upper_case = lambda s: s.strip().upper() strip_and_upper_case("

Hello

")

returns the string: HELLO

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They can also take arbitrary number of arguments / keyword arguments, like normal functions. greeting = lambda x, *args, **kwargs: print(x, args, kwargs) greeting('hello', 'world', world='world')

prints: hello ('world',) {'world': 'world'} lambdas are commonly used for short functions that are convenient to define at the point where they are called

(typically with sorted, filter and map). For example, this line sorts a list of strings ignoring their case and ignoring whitespace at the beginning and at the end: sorted( [" foo ", " # Out: # [' bAR', 'BaZ

bAR", "BaZ

"], key=lambda s: s.strip().upper())

', ' foo ']

Sort list just ignoring whitespaces: sorted( [" foo ", " bAR", "BaZ # Out: # ['BaZ ', ' bAR', ' foo ']

"], key=lambda s: s.strip())

Examples with map: sorted( map( lambda s: s.strip().upper(), [" foo ", " # Out: # ['BAR', 'BAZ', 'FOO'] sorted( map( lambda s: s.strip(), [" foo ", " # Out: # ['BaZ', 'bAR', 'foo']

bAR", "BaZ

bAR", "BaZ

"]))

"]))

Examples with numerical lists: my_list = [3, -4, -2, 5, 1, 7] sorted( my_list, key=lambda x: abs(x)) # Out: # [1, -2, 3, -4, 5, 7] list( filter( lambda x: x>0, my_list)) # Out: # [3, 5, 1, 7] list( map( lambda x: abs(x), my_list)) # Out: [3, 4, 2, 5, 1, 7]

One can call other functions (with/without arguments) from inside a lambda function. def foo(msg): print(msg) greet = lambda x = "hello world": foo(x) greet()

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prints: hello world

This is useful because lambda may contain only one expression and by using a subsidiary function one can run multiple statements. NOTE Bear in mind that PEP-8 (the official Python style guide) does not recommend assigning lambdas to variables (as we did in the first two examples): Always use a def statement instead of an assignment statement that binds a lambda expression directly to an identifier. Yes: def f(x): return 2*x

No: f = lambda x: 2*x

The first form means that the name of the resulting function object is specifically f instead of the generic . This is more useful for tracebacks and string representations in general. The use of the

assignment statement eliminates the sole benefit a lambda expression can offer over an explicit def statement (i.e. that it can be embedded inside a larger expression).

Section 33.4: Defining a function with optional arguments Optional arguments can be defined by assigning (using =) a default value to the argument-name: def make(action='nothing'): return action

Calling this function is possible in 3 different ways: make("fun") # Out: fun make(action="sleep") # Out: sleep # The argument is optional so the function will use the default value if the argument is # not passed in. make() # Out: nothing

Warning Mutable types (list, dict, set, etc.) should be treated with care when given as default attribute. Any mutation of the default argument will change it permanently. See Defining a function with optional mutable arguments. GoalKicker.com – Python® Notes for Professionals

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Section 33.5: Defining a function with optional mutable arguments There is a problem when using optional arguments with a mutable default type (described in Defining a function with optional arguments), which can potentially lead to unexpected behaviour. Explanation This problem arises because a function's default arguments are initialised once, at the point when the function is defined, and not (like many other languages) when the function is called. The default values are stored inside the function object's __defaults__ member variable. def f(a, b=42, c=[]): pass print(f.__defaults__) # Out: (42, [])

For immutable types (see Argument passing and mutability) this is not a problem because there is no way to mutate the variable; it can only ever be reassigned, leaving the original value unchanged. Hence, subsequent are guaranteed to have the same default value. However, for a mutable type, the original value can mutate, by making calls to its various member functions. Therefore, successive calls to the function are not guaranteed to have the initial default value. def append(elem, to=[]): to.append(elem) # This call to append() mutates the default variable "to" return to append(1) # Out: [1] append(2) # Appends it to the internally stored list # Out: [1, 2] append(3, []) # Out: [3]

# Using a new created list gives the expected result

# Calling it again without argument will append to the internally stored list again append(4) # Out: [1, 2, 4]

Note: Some IDEs like PyCharm will issue a warning when a mutable type is specified as a default attribute. Solution If you want to ensure that the default argument is always the one you specify in the function definition, then the solution is to always use an immutable type as your default argument. A common idiom to achieve this when a mutable type is needed as the default, is to use None (immutable) as the default argument and then assign the actual default value to the argument variable if it is equal to None. def append(elem, to=None): if to is None: to = []

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to.append(elem) return to

Section 33.6: Argument passing and mutability First, some terminology: argument (actual parameter): the actual variable being passed to a function; parameter (formal parameter): the receiving variable that is used in a function. In Python, arguments are passed by assignment (as opposed to other languages, where arguments can be passed by value/reference/pointer). Mutating a parameter will mutate the argument (if the argument's type is mutable). def foo(x): x[0] = 9 print(x) y = [4, 5, 6] foo(y) # Out: [9, 5, 6] print(y) # Out: [9, 5, 6]

# here x is the parameter # This mutates the list labelled by both x and y

# call foo with y as argument # list labelled by x has been mutated # list labelled by y has been mutated too

Reassigning the parameter won’t reassign the argument. def foo(x): x[0] = 9 x = [1, 2, 3] x[2] = 8

# # # #

here This x is This

x is the parameter, when we call foo(y) we assign y to x mutates the list labelled by both x and y now labeling a different list (y is unaffected) mutates x's list, not y's list

y = [4, 5, 6] foo(y) y # Out: [9, 5, 6]

# y is the argument, x is the parameter # Pretend that we wrote "x = y", then go to line 1

In Python, we don’t really assign values to variables, instead we bind (i.e. assign, attach) variables (considered as names) to objects. Immutable: Integers, strings, tuples, and so on. All operations make copies. Mutable: Lists, dictionaries, sets, and so on. Operations may or may not mutate. x = [3, 1, 9] y = x x.append(5) # Mutates the list labelled by x and y, both x and y are bound to [3, 1, 9] x.sort() # Mutates the list labelled by x and y (in-place sorting) x = x + [4] # Does not mutate the list (makes a copy for x only, not y) z = x # z is x ([1, 3, 9, 4]) x += [6] # Mutates the list labelled by both x and z (uses the extend function). x = sorted(x) # Does not mutate the list (makes a copy for x only). x # Out: [1, 3, 4, 5, 6, 9] y # Out: [1, 3, 5, 9] z

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# Out: [1, 3, 5, 9, 4, 6]

Section 33.7: Returning values from functions Functions can return a value that you can use directly: def give_me_five(): return 5 print(give_me_five()) # Out: 5

# Print the returned value

or save the value for later use: num = give_me_five() print(num) # Out: 5

# Print the saved returned value

or use the value for any operations: print(give_me_five() + 10) # Out: 15

If return is encountered in the function the function will be exited immediately and subsequent operations will not be evaluated: def give_me_another_five(): return 5 print('This statement will not be printed. Ever.') print(give_me_another_five()) # Out: 5

You can also return multiple values (in the form of a tuple): def give_me_two_fives(): return 5, 5 # Returns two 5 first, second = give_me_two_fives() print(first) # Out: 5 print(second) # Out: 5

A function with no return statement implicitly returns None. Similarly a function with a return statement, but no return value or variable returns None.

Section 33.8: Closure Closures in Python are created by function calls. Here, the call to makeInc creates a binding for x that is referenced inside the function inc. Each call to makeInc creates a new instance of this function, but each instance has a link to a different binding of x. def makeInc(x): def inc(y): # x is "attached" in the definition of inc

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return y + x return inc incOne = makeInc(1) incFive = makeInc(5) incOne(5) # returns 6 incFive(5) # returns 10

Notice that while in a regular closure the enclosed function fully inherits all variables from its enclosing environment, in this construct the enclosed function has only read access to the inherited variables but cannot make assignments to them def makeInc(x): def inc(y): # incrementing x is not allowed x += y return x return inc incOne = makeInc(1) incOne(5) # UnboundLocalError: local variable 'x' referenced before assignment

Python 3 offers the nonlocal statement (Nonlocal Variables ) for realizing a full closure with nested functions. Python 3.x Version

≥ 3.0

def makeInc(x): def inc(y): nonlocal x # now assigning a value to x is allowed x += y return x return inc incOne = makeInc(1) incOne(5) # returns 6

Section 33.9: Forcing the use of named parameters All parameters specified after the first asterisk in the function signature are keyword-only. def f(*a, b): pass f(1, 2, 3) # TypeError: f() missing 1 required keyword-only argument: 'b'

In Python 3 it's possible to put a single asterisk in the function signature to ensure that the remaining arguments may only be passed using keyword arguments. def f(a, b, *, c): pass f(1, 2, 3) # TypeError: f() takes 2 positional arguments but 3 were given

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f(1, 2, c=3) # No error

Section 33.10: Nested functions Functions in python are first-class objects. They can be defined in any scope def fibonacci(n): def step(a,b): return b, a+b a, b = 0, 1 for i in range(n): a, b = step(a, b) return a

Functions capture their enclosing scope can be passed around like any other sort of object def make_adder(n): def adder(x): return n + x return adder add5 = make_adder(5) add6 = make_adder(6) add5(10) #Out: 15 add6(10) #Out: 16 def repeatedly_apply(func, n, x): for i in range(n): x = func(x) return x repeatedly_apply(add5, 5, 1) #Out: 26

Section 33.11: Recursion limit There is a limit to the depth of possible recursion, which depends on the Python implementation. When the limit is reached, a RuntimeError exception is raised: def cursing(depth): try: cursing(depth + 1) # actually, re-cursing except RuntimeError as RE: print('I recursed {} times!'.format(depth)) cursing(0) # Out: I recursed 1083 times!

It is possible to change the recursion depth limit by using sys.setrecursionlimit(limit) and check this limit by sys.getrecursionlimit(). sys.setrecursionlimit(2000) cursing(0) # Out: I recursed 1997 times!

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From Python 3.5, the exception is a RecursionError, which is derived from RuntimeError.

Section 33.12: Recursive Lambda using assigned variable One method for creating recursive lambda functions involves assigning the function to a variable and then referencing that variable within the function itself. A common example of this is the recursive calculation of the factorial of a number - such as shown in the following code: lambda_factorial = lambda i:1 if i==0 else i*lambda_factorial(i-1) print(lambda_factorial(4)) # 4 * 3 * 2 * 1 = 12 * 2 = 24

Description of code The lambda function, through its variable assignment, is passed a value (4) which it evaluates and returns 1 if it is 0 or else it returns the current value (i) * another calculation by the lambda function of the value - 1 (i-1). This continues until the passed value is decremented to 0 (return 1). A process which can be visualized as:

Section 33.13: Recursive functions A recursive function is a function that calls itself in its definition. For example the mathematical function, factorial, defined by factorial(n) = n*(n-1)*(n-2)*...*3*2*1. can be programmed as

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def factorial(n): #n here should be an integer if n == 0: return 1 else: return n*factorial(n-1)

the outputs here are: factorial(0) #out 1 factorial(1) #out 1 factorial(2) #out 2 factorial(3) #out 6

as expected. Notice that this function is recursive because the second return factorial(n-1), where the function calls itself in its definition. Some recursive functions can be implemented using lambda, the factorial function using lambda would be something like this: factorial = lambda n: 1 if n == 0 else n*factorial(n-1)

The function outputs the same as above.

Section 33.14: Defining a function with arguments Arguments are defined in parentheses after the function name: def divide(dividend, divisor): # The names of the function and its arguments # The arguments are available by name in the body of the function print(dividend / divisor)

The function name and its list of arguments are called the signature of the function. Each named argument is effectively a local variable of the function. When calling the function, give values for the arguments by listing them in order divide(10, 2) # output: 5

or specify them in any order using the names from the function definition: divide(divisor=2, dividend=10) # output: 5

Section 33.15: Iterable and dictionary unpacking Functions allow you to specify these types of parameters: positional, named, variable positional, Keyword args (kwargs). Here is a clear and concise use of each type. def unpacking(a, b, c=45, d=60, *args, **kwargs):

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print(a, b, c, d, args, kwargs) >>> 1 2 >>> 1 2 >>> 1 2 >>> 1 2

unpacking(1, 45 60 () {} unpacking(1, 3 4 () {} unpacking(1, 3 4 () {} unpacking(1, 3 4 () {}

2) 2, 3, 4) 2, c=3, d=4) 2, d=4, c=3)

>>> pair = (3,) >>> unpacking(1, 2, *pair, d=4) 1 2 3 4 () {} >>> unpacking(1, 2, d=4, *pair) 1 2 3 4 () {} >>> unpacking(1, 2, *pair, c=3) Traceback (most recent call last): File "", line 1, in TypeError: unpacking() got multiple values for argument 'c' >>> unpacking(1, 2, c=3, *pair) Traceback (most recent call last): File "", line 1, in TypeError: unpacking() got multiple values for argument 'c' >>> args_list = [3] >>> unpacking(1, 2, *args_list, d=4) 1 2 3 4 () {} >>> unpacking(1, 2, d=4, *args_list) 1 2 3 4 () {} >>> unpacking(1, 2, c=3, *args_list) Traceback (most recent call last): File "", line 1, in TypeError: unpacking() got multiple values for argument 'c' >>> unpacking(1, 2, *args_list, c=3) Traceback (most recent call last): File "", line 1, in TypeError: unpacking() got multiple values for argument 'c'

>>> pair = (3, 4) >>> unpacking(1, 2, *pair) 1 2 3 4 () {} >>> unpacking(1, 2, 3, 4, *pair) 1 2 3 4 (3, 4) {} >>> unpacking(1, 2, d=4, *pair) Traceback (most recent call last): File "", line 1, in TypeError: unpacking() got multiple values for argument 'd' >>> unpacking(1, 2, *pair, d=4) Traceback (most recent call last): File "", line 1, in TypeError: unpacking() got multiple values for argument 'd'

>>> >>> 1 2 >>> 1 2

args_list = [3, 4] unpacking(1, 2, *args_list) 3 4 () {} unpacking(1, 2, 3, 4, *args_list) 3 4 (3, 4) {}

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>>> unpacking(1, 2, d=4, *args_list) Traceback (most recent call last): File "", line 1, in TypeError: unpacking() got multiple values for argument 'd' >>> unpacking(1, 2, *args_list, d=4) Traceback (most recent call last): File "", line 1, in TypeError: unpacking() got multiple values for argument 'd'

>>> >>> 1 2 >>> >>> 1 2 >>> >>> 1 2

arg_dict = {'c':3, 'd':4} unpacking(1, 2, **arg_dict) 3 4 () {} arg_dict = {'d':4, 'c':3} unpacking(1, 2, **arg_dict) 3 4 () {} arg_dict = {'c':3, 'd':4, 'not_a_parameter': 75} unpacking(1, 2, **arg_dict) 3 4 () {'not_a_parameter': 75}

>>> unpacking(1, 2, *pair, **arg_dict) Traceback (most recent call last): File "", line 1, in TypeError: unpacking() got multiple values for argument 'd' >>> unpacking(1, 2, 3, 4, **arg_dict) Traceback (most recent call last): File "", line 1, in TypeError: unpacking() got multiple values for argument 'd' # Positional arguments take priority over any other form of argument passing >>> unpacking(1, 2, **arg_dict, c=3) 1 2 3 4 () {'not_a_parameter': 75} >>> unpacking(1, 2, 3, **arg_dict, c=3) Traceback (most recent call last): File "", line 1, in TypeError: unpacking() got multiple values for argument 'c'

Section 33.16: Defining a function with multiple arguments One can give a function as many arguments as one wants, the only fixed rules are that each argument name must be unique and that optional arguments must be after the not-optional ones: def func(value1, value2, optionalvalue=10): return '{0} {1} {2}'.format(value1, value2, optionalvalue1)

When calling the function you can either give each keyword without the name but then the order matters: print(func(1, 'a', 100)) # Out: 1 a 100 print(func('abc', 14)) # abc 14 10

Or combine giving the arguments with name and without. Then the ones with name must follow those without but the order of the ones with name doesn't matter: print(func('This', optionalvalue='StackOverflow Documentation', value2='is')) # Out: This is StackOverflow Documentation

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Chapter 34: Defining functions with list arguments Section 34.1: Function and Call Lists as arguments are just another variable: def func(myList): for item in myList: print(item)

and can be passed in the function call itself: func([1,2,3,5,7]) 1 2 3 5 7

Or as a variable: aList = ['a','b','c','d'] func(aList) a b c d

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Chapter 35: Functional Programming in Python Functional programming decomposes a problem into a set of functions. Ideally, functions only take inputs and produce outputs, and don’t have any internal state that affects the output produced for a given input.below are functional techniques common to many languages: such as lambda, map, reduce.

Section 35.1: Lambda Function An anonymous, inlined function defined with lambda. The parameters of the lambda are defined to the left of the colon. The function body is defined to the right of the colon. The result of running the function body is (implicitly) returned. s=lambda x:x*x s(2) =>4

Section 35.2: Map Function Map takes a function and a collection of items. It makes a new, empty collection, runs the function on each item in the original collection and inserts each return value into the new collection. It returns the new collection. This is a simple map that takes a list of names and returns a list of the lengths of those names: name_lengths = map(len, ["Mary", "Isla", "Sam"]) print(name_lengths) =>[4, 4, 3]

Section 35.3: Reduce Function Reduce takes a function and a collection of items. It returns a value that is created by combining the items. This is a simple reduce. It returns the sum of all the items in the collection. total = reduce(lambda a, x: a + x, [0, 1, 2, 3, 4]) print(total) =>10

Section 35.4: Filter Function Filter takes a function and a collection. It returns a collection of every item for which the function returned True. arr=[1,2,3,4,5,6] [i for i in filter(lambda x:x>4,arr)]

# outputs[5,6]

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Chapter 36: Partial functions Param details x the number to be raised y

the exponent

raise

the function to be specialized

As you probably know if you came from OOP school, specializing an abstract class and use it is a practice you should keep in mind when writing your code. What if you could define an abstract function and specialize it in order to create different versions of it? Thinks it as a sort of function Inheritance where you bind specific params to make them reliable for a specific scenario.

Section 36.1: Raise the power Let's suppose we want raise x to a number y. You'd write this as: def raise_power(x, y): return x**y

What if your y value can assume a finite set of values? Let's suppose y can be one of [3,4,5] and let's say you don't want offer end user the possibility to use such function since it is very computationally intensive. In fact you would check if provided y assumes a valid value and rewrite your function as: def raise(x, y): if y in (3,4,5): return x**y raise NumberNotInRangeException("You should provide a valid exponent")

Messy? Let's use the abstract form and specialize it to all three cases: let's implement them partially. from functors import partial raise_to_three = partial(raise, y=3) raise_to_four = partial(raise, y=4) raise_to_five = partial(raise, y=5)

What happens here? We fixed the y params and we defined three different functions. No need to use the abstract function defined above (you could make it private) but you could use partial applied functions to deal with raising a number to a fixed value.

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Chapter 37: Decorators Parameter Details f The function to be decorated (wrapped) Decorator functions are software design patterns. They dynamically alter the functionality of a function, method, or class without having to directly use subclasses or change the source code of the decorated function. When used correctly, decorators can become powerful tools in the development process. This topic covers implementation and applications of decorator functions in Python.

Section 37.1: Decorator function Decorators augment the behavior of other functions or methods. Any function that takes a function as a parameter and returns an augmented function can be used as a decorator. # This simplest decorator does nothing to the function being decorated. Such # minimal decorators can occasionally be used as a kind of code markers. def super_secret_function(f): return f @super_secret_function def my_function(): print("This is my secret function.")

The @-notation is syntactic sugar that is equivalent to the following: my_function = super_secret_function(my_function)

It is important to bear this in mind in order to understand how the decorators work. This "unsugared" syntax makes it clear why the decorator function takes a function as an argument, and why it should return another function. It also demonstrates what would happen if you don't return a function: def disabled(f): """ This function returns nothing, and hence removes the decorated function from the local scope. """ pass @disabled def my_function(): print("This function can no longer be called...") my_function() # TypeError: 'NoneType' object is not callable

Thus, we usually define a new function inside the decorator and return it. This new function would first do something that it needs to do, then call the original function, and finally process the return value. Consider this simple decorator function that prints the arguments that the original function receives, then calls it. #This is the decorator def print_args(func): def inner_func(*args, **kwargs): print(args) print(kwargs)

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return func(*args, **kwargs) #Call the original function with its arguments. return inner_func @print_args def multiply(num_a, num_b): return num_a * num_b print(multiply(3, 5)) #Output: # (3,5) - This is actually the 'args' that the function receives. # {} - This is the 'kwargs', empty because we didn't specify keyword arguments. # 15 - The result of the function.

Section 37.2: Decorator class As mentioned in the introduction, a decorator is a function that can be applied to another function to augment its behavior. The syntactic sugar is equivalent to the following: my_func = decorator(my_func). But what if the decorator was instead a class? The syntax would still work, except that now my_func gets replaced with an instance

of the decorator class. If this class implements the __call__() magic method, then it would still be possible to use my_func as if it was a function: class Decorator(object): """Simple decorator class.""" def __init__(self, func): self.func = func def __call__(self, *args, **kwargs): print('Before the function call.') res = self.func(*args, **kwargs) print('After the function call.') return res @Decorator def testfunc(): print('Inside the function.') testfunc() # Before the function call. # Inside the function. # After the function call.

Note that a function decorated with a class decorator will no longer be considered a "function" from type-checking perspective: import types isinstance(testfunc, types.FunctionType) # False type(testfunc) #

Decorating Methods For decorating methods you need to define an additional __get__-method: from types import MethodType class Decorator(object): def __init__(self, func):

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self.func = func def __call__(self, *args, **kwargs): print('Inside the decorator.') return self.func(*args, **kwargs) def __get__(self, instance, cls): # Return a Method if it is called on an instance return self if instance is None else MethodType(self, instance) class Test(object): @Decorator def __init__(self): pass a = Test()

Inside the decorator. Warning! Class Decorators only produce one instance for a specific function so decorating a method with a class decorator will share the same decorator between all instances of that class: from types import MethodType class CountCallsDecorator(object): def __init__(self, func): self.func = func self.ncalls = 0 # Number of calls of this method def __call__(self, *args, **kwargs): self.ncalls += 1 # Increment the calls counter return self.func(*args, **kwargs) def __get__(self, instance, cls): return self if instance is None else MethodType(self, instance) class Test(object): def __init__(self): pass @CountCallsDecorator def do_something(self): return 'something was done' a = Test() a.do_something() a.do_something.ncalls b = Test() b.do_something() b.do_something.ncalls

# 1

# 2

Section 37.3: Decorator with arguments (decorator factory) A decorator takes just one argument: the function to be decorated. There is no way to pass other arguments. But additional arguments are often desired. The trick is then to make a function which takes arbitrary arguments GoalKicker.com – Python® Notes for Professionals

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and returns a decorator. Decorator functions def decoratorfactory(message): def decorator(func): def wrapped_func(*args, **kwargs): print('The decorator wants to tell you: {}'.format(message)) return func(*args, **kwargs) return wrapped_func return decorator @decoratorfactory('Hello World') def test(): pass test()

The decorator wants to tell you: Hello World Important Note: With such decorator factories you must call the decorator with a pair of parentheses: @decoratorfactory # Without parentheses def test(): pass test()

TypeError: decorator() missing 1 required positional argument: 'func' Decorator classes def decoratorfactory(*decorator_args, **decorator_kwargs): class Decorator(object): def __init__(self, func): self.func = func def __call__(self, *args, **kwargs): print('Inside the decorator with arguments {}'.format(decorator_args)) return self.func(*args, **kwargs) return Decorator @decoratorfactory(10) def test(): pass test()

Inside the decorator with arguments (10,)

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Section 37.4: Making a decorator look like the decorated function Decorators normally strip function metadata as they aren't the same. This can cause problems when using metaprogramming to dynamically access function metadata. Metadata also includes function's docstrings and its name. functools.wraps makes the decorated function look like the original function by copying several attributes to the

wrapper function. from functools import wraps

The two methods of wrapping a decorator are achieving the same thing in hiding that the original function has been decorated. There is no reason to prefer the function version to the class version unless you're already using one over the other. As a function def decorator(func): # Copies the docstring, name, annotations and module to the decorator @wraps(func) def wrapped_func(*args, **kwargs): return func(*args, **kwargs) return wrapped_func @decorator def test(): pass test.__name__

'test' As a class class Decorator(object): def __init__(self, func): # Copies name, module, annotations and docstring to the instance. self._wrapped = wraps(func)(self) def __call__(self, *args, **kwargs): return self._wrapped(*args, **kwargs) @Decorator def test(): """Docstring of test.""" pass test.__doc__

'Docstring of test.'

Section 37.5: Using a decorator to time a function import time def timer(func): def inner(*args, **kwargs):

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t1 = time.time() f = func(*args, **kwargs) t2 = time.time() print 'Runtime took {0} seconds'.format(t2-t1) return f return inner @timer def example_function(): #do stuff

example_function()

Section 37.6: Create singleton class with a decorator A singleton is a pattern that restricts the instantiation of a class to one instance/object. Using a decorator, we can define a class as a singleton by forcing the class to either return an existing instance of the class or create a new instance (if it doesn't exist). def singleton(cls): instance = [None] def wrapper(*args, **kwargs): if instance[0] is None: instance[0] = cls(*args, **kwargs) return instance[0] return wrapper

This decorator can be added to any class declaration and will make sure that at most one instance of the class is created. Any subsequent calls will return the already existing class instance. @singleton class SomeSingletonClass: x = 2 def __init__(self): print("Created!") instance = SomeSingletonClass() instance = SomeSingletonClass() print(instance.x)

# prints: Created! # doesn't print anything # 2

instance.x = 3 print(SomeSingletonClass().x)

# 3

So it doesn't matter whether you refer to the class instance via your local variable or whether you create another "instance", you always get the same object.

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Chapter 38: Classes Python offers itself not only as a popular scripting language, but also supports the object-oriented programming paradigm. Classes describe data and provide methods to manipulate that data, all encompassed under a single object. Furthermore, classes allow for abstraction by separating concrete implementation details from abstract representations of data. Code utilizing classes is generally easier to read, understand, and maintain.

Section 38.1: Introduction to classes A class, functions as a template that defines the basic characteristics of a particular object. Here's an example: class Person(object): """A simple class.""" species = "Homo Sapiens"

# docstring # class attribute

def __init__(self, name): """This is the initializer. It's a special method (see below). """ self.name = name

# special method

def __str__(self): """This method is run when Python tries to cast the object to a string. Return this string when using print(), etc. """ return self.name

# special method

# instance attribute

def rename(self, renamed): # regular method """Reassign and print the name attribute.""" self.name = renamed print("Now my name is {}".format(self.name))

There are a few things to note when looking at the above example. 1. The class is made up of attributes (data) and methods (functions). 2. Attributes and methods are simply defined as normal variables and functions. 3. As noted in the corresponding docstring, the __init__() method is called the initializer. It's equivalent to the constructor in other object oriented languages, and is the method that is first run when you create a new object, or new instance of the class. 4. Attributes that apply to the whole class are defined first, and are called class attributes. 5. Attributes that apply to a specific instance of a class (an object) are called instance attributes. They are generally defined inside __init__(); this is not necessary, but it is recommended (since attributes defined outside of __init__() run the risk of being accessed before they are defined). 6. Every method, included in the class definition passes the object in question as its first parameter. The word self is used for this parameter (usage of self is actually by convention, as the word self has no inherent

meaning in Python, but this is one of Python's most respected conventions, and you should always follow it). 7. Those used to object-oriented programming in other languages may be surprised by a few things. One is that Python has no real concept of private elements, so everything, by default, imitates the behavior of the C++/Java public keyword. For more information, see the "Private Class Members" example on this page. 8. Some of the class's methods have the following form: __functionname__(self, other_stuff). All such methods are called "magic methods" and are an important part of classes in Python. For instance, operator overloading in Python is implemented with magic methods. For more information, see the relevant GoalKicker.com – Python® Notes for Professionals

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documentation. Now let's make a few instances of our Person class! >>> >>> >>> >>>

# Instances kelly = Person("Kelly") joseph = Person("Joseph") john_doe = Person("John Doe")

We currently have three Person objects, kelly, joseph, and john_doe. We can access the attributes of the class from each instance using the dot operator . Note again the difference between class and instance attributes: >>> # Attributes >>> kelly.species 'Homo Sapiens' >>> john_doe.species 'Homo Sapiens' >>> joseph.species 'Homo Sapiens' >>> kelly.name 'Kelly' >>> joseph.name 'Joseph'

We can execute the methods of the class using the same dot operator .: >>> # Methods >>> john_doe.__str__() 'John Doe' >>> print(john_doe) 'John Doe' >>> john_doe.rename("John") 'Now my name is John'

Section 38.2: Bound, unbound, and static methods The idea of bound and unbound methods was removed in Python 3. In Python 3 when you declare a method within a class, you are using a def keyword, thus creating a function object. This is a regular function, and the surrounding class works as its namespace. In the following example we declare method f within class A, and it becomes a function A.f: Python 3.x Version

≥ 3.0

class A(object): def f(self, x): return 2 * x A.f #

(in Python 3.x)

In Python 2 the behavior was different: function objects within the class were implicitly replaced with objects of type instancemethod, which were called unbound methods because they were not bound to any particular class instance.

It was possible to access the underlying function using .__func__ property. Python 2.x Version

≥ 2.3

A.f

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# (in Python 2.x) A.f.__class__ # A.f.__func__ #

The latter behaviors are confirmed by inspection - methods are recognized as functions in Python 3, while the distinction is upheld in Python 2. Python 3.x Version

≥ 3.0

import inspect inspect.isfunction(A.f) # True inspect.ismethod(A.f) # False

Python 2.x Version

≥ 2.3

import inspect inspect.isfunction(A.f) # False inspect.ismethod(A.f) # True

In both versions of Python function/method A.f can be called directly, provided that you pass an instance of class A as the first argument. A.f(1, 7) # Python 2: TypeError: unbound method f() must be called with # A instance as first argument (got int instance instead) # Python 3: 14 a = A() A.f(a, 20) # Python 2 & 3: 40

Now suppose a is an instance of class A, what is a.f then? Well, intuitively this should be the same method f of class A, only it should somehow "know" that it was applied to the object a – in Python this is called method bound to a.

The nitty-gritty details are as follows: writing a.f invokes the magic __getattribute__ method of a, which first checks whether a has an attribute named f (it doesn't), then checks the class A whether it contains a method with such a name (it does), and creates a new object m of type method which has the reference to the original A.f in m.__func__, and a reference to the object a in m.__self__. When this object is called as a function, it simply does

the following: m(...) => m.__func__(m.__self__, ...). Thus this object is called a bound method because when invoked it knows to supply the object it was bound to as the first argument. (These things work same way in Python 2 and 3). a = A() a.f # a.f(2) # 4 # Note: the bound method object a.f is recreated *every time* you call it: a.f is a.f # False # As a performance optimization you can store the bound method in the object's # __dict__, in which case the method object will remain fixed: a.f = a.f

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a.f is a.f

# True

Finally, Python has class methods and static methods – special kinds of methods. Class methods work the same way as regular methods, except that when invoked on an object they bind to the class of the object instead of to the object. Thus m.__self__ = type(a). When you call such bound method, it passes the class of a as the first argument. Static methods are even simpler: they don't bind anything at all, and simply return the underlying function without any transformations. class D(object): multiplier = 2 @classmethod def f(cls, x): return cls.multiplier * x @staticmethod def g(name): print("Hello, %s" % name) D.f # D.f(12) # 24 D.g # D.g("world") # Hello, world

Note that class methods are bound to the class even when accessed on the instance: d = D() d.multiplier = 1337 (D.multiplier, d.multiplier) # (2, 1337) d.f # d.f(10) # 20

It is worth noting that at the lowest level, functions, methods, staticmethods, etc. are actually descriptors that invoke __get__, __set__ and optionally __del__ special methods. For more details on classmethods and staticmethods: What is the difference between @staticmethod and @classmethod in Python? Meaning of @classmethod and @staticmethod for beginner?

Section 38.3: Basic inheritance Inheritance in Python is based on similar ideas used in other object oriented languages like Java, C++ etc. A new class can be derived from an existing class as follows. class BaseClass(object): pass class DerivedClass(BaseClass): pass

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The BaseClass is the already existing (parent) class, and the DerivedClass is the new (child) class that inherits (or subclasses) attributes from BaseClass. Note: As of Python 2.2, all classes implicitly inherit from the object class, which is the base class for all built-in types. We define a parent Rectangle class in the example below, which implicitly inherits from object: class Rectangle(): def __init__(self, w, h): self.w = w self.h = h def area(self): return self.w * self.h def perimeter(self): return 2 * (self.w + self.h)

The Rectangle class can be used as a base class for defining a Square class, as a square is a special case of rectangle. class Square(Rectangle): def __init__(self, s): # call parent constructor, w and h are both s super(Square, self).__init__(s, s) self.s = s

The Square class will automatically inherit all attributes of the Rectangle class as well as the object class. super() is used to call the __init__() method of Rectangle class, essentially calling any overridden method of the base class. Note: in Python 3, super() does not require arguments. Derived class objects can access and modify the attributes of its base classes: r.area() # Output: 12 r.perimeter() # Output: 14 s.area() # Output: 4 s.perimeter() # Output: 8

Built-in functions that work with inheritance issubclass(DerivedClass, BaseClass): returns True if DerivedClass is a subclass of the BaseClass isinstance(s, Class): returns True if s is an instance of Class or any of the derived classes of Class # subclass check issubclass(Square, Rectangle) # Output: True # instantiate r = Rectangle(3, 4) s = Square(2) isinstance(r, Rectangle) # Output: True isinstance(r, Square)

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# Output: False # A rectangle is not a square isinstance(s, Rectangle) # Output: True # A square is a rectangle isinstance(s, Square) # Output: True

Section 38.4: Monkey Patching In this case, "monkey patching" means adding a new variable or method to a class after it's been defined. For instance, say we defined class A as class A(object): def __init__(self, num): self.num = num def __add__(self, other): return A(self.num + other.num)

But now we want to add another function later in the code. Suppose this function is as follows. def get_num(self): return self.num

But how do we add this as a method in A? That's simple we just essentially place that function into A with an assignment statement. A.get_num = get_num

Why does this work? Because functions are objects just like any other object, and methods are functions that belong to the class. The function get_num shall be available to all existing (already created) as well to the new instances of A These additions are available on all instances of that class (or its subclasses) automatically. For example: foo = A(42) A.get_num = get_num bar = A(6); foo.get_num() # 42 bar.get_num() # 6

Note that, unlike some other languages, this technique does not work for certain built-in types, and it is not considered good style.

Section 38.5: New-style vs. old-style classes Python 2.x Version

≥ 2.2.0

New-style classes were introduced in Python 2.2 to unify classes and types. They inherit from the top-level object GoalKicker.com – Python® Notes for Professionals

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type. A new-style class is a user-defined type, and is very similar to built-in types. # new-style class class New(object): pass # new-style instance new = New() new.__class__ # type(new) # issubclass(New, object) # True

Old-style classes do not inherit from object. Old-style instances are always implemented with a built-in instance type. # old-style class class Old: pass # old-style instance old = Old() old.__class__ # type(old) # issubclass(Old, object) # False

Python 3.x Version

≥ 3.0.0

In Python 3, old-style classes were removed. New-style classes in Python 3 implicitly inherit from object, so there is no need to specify MyClass(object) anymore. class MyClass: pass my_inst = MyClass() type(my_inst) # my_inst.__class__ # issubclass(MyClass, object) # True

Section 38.6: Class methods: alternate initializers Class methods present alternate ways to build instances of classes. To illustrate, let's look at an example. Let's suppose we have a relatively simple Person class: class Person(object):

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def __init__(self, first_name, last_name, age): self.first_name = first_name self.last_name = last_name self.age = age self.full_name = first_name + " " + last_name def greet(self): print("Hello, my name is " + self.full_name + ".")

It might be handy to have a way to build instances of this class specifying a full name instead of first and last name separately. One way to do this would be to have last_name be an optional parameter, and assuming that if it isn't given, we passed the full name in: class Person(object): def __init__(self, first_name, age, last_name=None): if last_name is None: self.first_name, self.last_name = first_name.split(" ", 2) else: self.first_name = first_name self.last_name = last_name self.full_name = self.first_name + " " + self.last_name self.age = age def greet(self): print("Hello, my name is " + self.full_name + ".")

However, there are two main problems with this bit of code: 1. The parameters first_name and last_name are now misleading, since you can enter a full name for first_name. Also, if there are more cases and/or more parameters that have this kind of flexibility, the

if/elif/else branching can get annoying fast. 2. Not quite as important, but still worth pointing out: what if last_name is None, but first_name doesn't split into two or more things via spaces? We have yet another layer of input validation and/or exception handling... Enter class methods. Rather than having a single initializer, we will create a separate initializer, called from_full_name, and decorate it with the (built-in) classmethod decorator. class Person(object): def __init__(self, first_name, last_name, age): self.first_name = first_name self.last_name = last_name self.age = age self.full_name = first_name + " " + last_name @classmethod def from_full_name(cls, name, age): if " " not in name: raise ValueError first_name, last_name = name.split(" ", 2) return cls(first_name, last_name, age) def greet(self): print("Hello, my name is " + self.full_name + ".")

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Notice cls instead of self as the first argument to from_full_name. Class methods are applied to the overall class, not an instance of a given class (which is what self usually denotes). So, if cls is our Person class, then the returned value from the from_full_name class method is Person(first_name, last_name, age), which uses Person's __init__ to create an instance of the Person class. In particular, if we were to make a subclass Employee of Person,

then from_full_name would work in the Employee class as well. To show that this works as expected, let's create instances of Person in more than one way without the branching in __init__: In [2]: bob = Person("Bob", "Bobberson", 42) In [3]: alice = Person.from_full_name("Alice Henderson", 31) In [4]: bob.greet() Hello, my name is Bob Bobberson. In [5]: alice.greet() Hello, my name is Alice Henderson.

Other references: Python @classmethod and @staticmethod for beginner? https://docs.python.org/2/library/functions.html#classmethod https://docs.python.org/3.5/library/functions.html#classmethod

Section 38.7: Multiple Inheritance Python uses the C3 linearization algorithm to determine the order in which to resolve class attributes, including methods. This is known as the Method Resolution Order (MRO). Here's a simple example: class Foo(object): foo = 'attr foo of Foo'

class Bar(object): foo = 'attr foo of Bar' # we won't see this. bar = 'attr bar of Bar' class FooBar(Foo, Bar): foobar = 'attr foobar of FooBar'

Now if we instantiate FooBar, if we look up the foo attribute, we see that Foo's attribute is found first fb = FooBar()

and >>> fb.foo 'attr foo of Foo'

Here's the MRO of FooBar:

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>>> FooBar.mro() [, , , ]

It can be simply stated that Python's MRO algorithm is 1. Depth first (e.g. FooBar then Foo) unless 2. a shared parent (object) is blocked by a child (Bar) and 3. no circular relationships allowed. That is, for example, Bar cannot inherit from FooBar while FooBar inherits from Bar. For a comprehensive example in Python, see the wikipedia entry. Another powerful feature in inheritance is super. super can fetch parent classes features. class Foo(object): def foo_method(self): print "foo Method" class Bar(object): def bar_method(self): print "bar Method" class FooBar(Foo, Bar): def foo_method(self): super(FooBar, self).foo_method()

Multiple inheritance with init method of class, when every class has own init method then we try for multiple inheritance then only init method get called of class which is inherit first. for below example only Foo class init method getting called Bar class init not getting called class Foo(object): def __init__(self): print "foo init" class Bar(object): def __init__(self): print "bar init" class FooBar(Foo, Bar): def __init__(self): print "foobar init" super(FooBar, self).__init__() a = FooBar()

Output: foobar init foo init

But it doesn't mean that Bar class is not inherit. Instance of final FooBar class is also instance of Bar class and Foo class. print isinstance(a,FooBar) print isinstance(a,Foo)

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print isinstance(a,Bar)

Output: True True True

Section 38.8: Properties Python classes support properties, which look like regular object variables, but with the possibility of attaching custom behavior and documentation. class MyClass(object): def __init__(self): self._my_string = "" @property def string(self): """A profoundly important string.""" return self._my_string @string.setter def string(self, new_value): assert isinstance(new_value, str), \ "Give me a string, not a %r!" % type(new_value) self._my_string = new_value @string.deleter def x(self): self._my_string = None

The object's of class MyClass will appear to have a property .string, however it's behavior is now tightly controlled: mc = MyClass() mc.string = "String!" print(mc.string) del mc.string

As well as the useful syntax as above, the property syntax allows for validation, or other augmentations to be added to those attributes. This could be especially useful with public APIs - where a level of help should be given to the user. Another common use of properties is to enable the class to present 'virtual attributes' - attributes which aren't actually stored but are computed only when requested. class Character(object): def __init__(name, max_hp): self._name = name self._hp = max_hp self._max_hp = max_hp # Make hp read only by not providing a set method @property def hp(self): return self._hp

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# Make name read only by not providing a set method @property def name(self): return self.name def take_damage(self, damage): self.hp -= damage self.hp = 0 if self.hp 0 else False @property def is_dead(self): return not self.is_alive bilbo = Character('Bilbo Baggins', 100) bilbo.hp # out : 100 bilbo.hp = 200 # out : AttributeError: can't set attribute # hp attribute is read only. bilbo.is_alive # out : True bilbo.is_wounded # out : False bilbo.is_dead # out : False bilbo.take_damage( 50 ) bilbo.hp # out : 50 bilbo.is_alive # out : True bilbo.is_wounded # out : True bilbo.is_dead # out : False bilbo.take_damage( 50 ) bilbo.hp # out : 0 bilbo.is_alive # out : False bilbo.is_wounded # out : False bilbo.is_dead # out : True

Section 38.9: Default values for instance variables If the variable contains a value of an immutable type (e.g. a string) then it is okay to assign a default value like this GoalKicker.com – Python® Notes for Professionals

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class Rectangle(object): def __init__(self, width, height, color='blue'): self.width = width self.height = height self.color = color def area(self): return self.width

* self.height

# Create some instances of the class default_rectangle = Rectangle(2, 3) print(default_rectangle.color) # blue red_rectangle = Rectangle(2, 3, 'red') print(red_rectangle.color) # red

One needs to be careful when initializing mutable objects such as lists in the constructor. Consider the following example: class Rectangle2D(object): def __init__(self, width, height, pos=[0,0], color='blue'): self.width = width self.height = height self.pos = pos self.color = color r1 = Rectangle2D(5,3) r2 = Rectangle2D(7,8) r1.pos[0] = 4 r1.pos # [4, 0] r2.pos # [4, 0] r2's pos has changed as well

This behavior is caused by the fact that in Python default parameters are bound at function execution and not at function declaration. To get a default instance variable that's not shared among instances, one should use a construct like this: class Rectangle2D(object): def __init__(self, width, height, pos=None, color='blue'): self.width = width self.height = height self.pos = pos or [0, 0] # default value is [0, 0] self.color = color r1 = Rectangle2D(5,3) r2 = Rectangle2D(7,8) r1.pos[0] = 4 r1.pos # [4, 0] r2.pos # [0, 0] r2's pos hasn't changed

See also Mutable Default Arguments and “Least Astonishment” and the Mutable Default Argument.

Section 38.10: Class and instance variables Instance variables are unique for each instance, while class variables are shared by all instances. class C: x = 2

# class variable

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def __init__(self, y): self.y = y # instance variable C.x # 2 C.y # AttributeError: type object 'C' has no attribute 'y' c1 = C(3) c1.x # 2 c1.y # 3 c2 = C(4) c2.x # 2 c2.y # 4

Class variables can be accessed on instances of this class, but assigning to the class attribute will create an instance variable which shadows the class variable c2.x = 4 c2.x # 4 C.x # 2

Note that mutating class variables from instances can lead to some unexpected consequences. class D: x = [] def __init__(self, item): self.x.append(item) # note that this is not an assignment! d1 = D(1) d2 = D(2) d1.x # [1, 2] d2.x # [1, 2] D.x # [1, 2]

Section 38.11: Class composition Class composition allows explicit relations between objects. In this example, people live in cities that belong to countries. Composition allows people to access the number of all people living in their country: class Country(object): def __init__(self): self.cities=[] def addCity(self,city): self.cities.append(city)

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class City(object): def __init__(self, numPeople): self.people = [] self.numPeople = numPeople

def addPerson(self, person): self.people.append(person) def join_country(self,country): self.country = country country.addCity(self) for i in range(self.numPeople): person(i).join_city(self)

class Person(object): def __init__(self, ID): self.ID=ID def join_city(self, city): self.city = city city.addPerson(self) def people_in_my_country(self): x= sum([len(c.people) for c in self.city.country.cities]) return x US=Country() NYC=City(10).join_country(US) SF=City(5).join_country(US) print(US.cities[0].people[0].people_in_my_country()) # 15

Section 38.12: Listing All Class Members The dir() function can be used to get a list of the members of a class: dir(Class)

For example: >>> dir(list) ['__add__', '__class__', '__contains__', '__delattr__', '__delitem__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__iadd__', '__imul__', '__init__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__reversed__', '__rmul__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', 'append', 'clear', 'copy', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']

It is common to look only for "non-magic" members. This can be done using a simple comprehension that lists members with names not starting with __: >>> [m for m in dir(list) if not m.startswith('__')] ['append', 'clear', 'copy', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']

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Caveats: Classes can define a __dir__() method. If that method exists calling dir() will call __dir__(), otherwise Python will try to create a list of members of the class. This means that the dir function can have unexpected results. Two quotes of importance from the official python documentation: If the object does not provide dir(), the function tries its best to gather information from the object’s dict attribute, if defined, and from its type object. The resulting list is not necessarily complete, and may be inaccurate when the object has a custom getattr().

Note: Because dir() is supplied primarily as a convenience for use at an interactive prompt, it tries to supply an interesting set of names more than it tries to supply a rigorously or consistently defined set of names, and its detailed behavior may change across releases. For example, metaclass attributes are not in the result list when the argument is a class.

Section 38.13: Singleton class A singleton is a pattern that restricts the instantiation of a class to one instance/object. For more info on python singleton design patterns, see here. class Singleton: def __new__(cls): try: it = cls.__it__ except AttributeError: it = cls.__it__ = object.__new__(cls) return it def __repr__(self): return ''.format(self.__class__.__name__.upper()) def __eq__(self, other): return other is self

Another method is to decorate your class. Following the example from this answer create a Singleton class: class Singleton: """ A non-thread-safe helper class to ease implementing singletons. This should be used as a decorator -- not a metaclass -- to the class that should be a singleton. The decorated class can define one `__init__` function that takes only the `self` argument. Other than that, there are no restrictions that apply to the decorated class. To get the singleton instance, use the `Instance` method. Trying to use `__call__` will result in a `TypeError` being raised. Limitations: The decorated class cannot be inherited from. """ def __init__(self, decorated):

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self._decorated = decorated def Instance(self): """ Returns the singleton instance. Upon its first call, it creates a new instance of the decorated class and calls its `__init__` method. On all subsequent calls, the already created instance is returned. """ try: return self._instance except AttributeError: self._instance = self._decorated() return self._instance def __call__(self): raise TypeError('Singletons must be accessed through `Instance()`.') def __instancecheck__(self, inst): return isinstance(inst, self._decorated)

To use you can use the Instance method @Singleton class Single: def __init__(self): self.name=None self.val=0 def getName(self): print(self.name) x=Single.Instance() y=Single.Instance() x.name='I\'m single' x.getName() # outputs I'm single y.getName() # outputs I'm single

Section 38.14: Descriptors and Dotted Lookups Descriptors are objects that are (usually) attributes of classes and that have any of __get__, __set__, or __delete__ special methods.

Data Descriptors have any of __set__, or __delete__ These can control the dotted lookup on an instance, and are used to implement functions, staticmethod, classmethod, and property. A dotted lookup (e.g. instance foo of class Foo looking up attribute bar - i.e. foo.bar)

uses the following algorithm: 1. bar is looked up in the class, Foo. If it is there and it is a Data Descriptor, then the data descriptor is used. That's how property is able to control access to data in an instance, and instances cannot override this. If a Data Descriptor is not there, then 2. bar is looked up in the instance __dict__. This is why we can override or block methods being called from an instance with a dotted lookup. If bar exists in the instance, it is used. If not, we then 3. look in the class Foo for bar. If it is a Descriptor, then the descriptor protocol is used. This is how functions (in this context, unbound methods), classmethod, and staticmethod are implemented. Else it simply returns the object there, or there is an AttributeError GoalKicker.com – Python® Notes for Professionals

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Chapter 39: Metaclasses Metaclasses allow you to deeply modify the behaviour of Python classes (in terms of how they're defined, instantiated, accessed, and more) by replacing the type metaclass that new classes use by default.

Section 39.1: Basic Metaclasses When type is called with three arguments it behaves as the (meta)class it is, and creates a new instance, ie. it produces a new class/type. Dummy = type('OtherDummy', (), dict(x=1)) Dummy.__class__ # Dummy().__class__.__class__ #

It is possible to subclass type to create an custom metaclass. class mytype(type): def __init__(cls, name, bases, dict): # call the base initializer type.__init__(cls, name, bases, dict) # perform custom initialization... cls.__custom_attribute__ = 2

Now, we have a new custom mytype metaclass which can be used to create classes in the same manner as type. MyDummy = mytype('MyDummy', (), dict(x=2)) MyDummy.__class__ # MyDummy().__class__.__class__ # MyDummy.__custom_attribute__ # 2

When we create a new class using the class keyword the metaclass is by default chosen based on upon the baseclasses. >>> class Foo(object): ... pass >>> type(Foo) type

In the above example the only baseclass is object so our metaclass will be the type of object, which is type. It is possible override the default, however it depends on whether we use Python 2 or Python 3: Python 2.x Version

≤ 2.7

A special class-level attribute __metaclass__ can be used to specify the metaclass. class MyDummy(object): __metaclass__ = mytype type(MyDummy) #

Python 3.x Version

≥ 3.0

A special metaclass keyword argument specify the metaclass. class MyDummy(metaclass=mytype):

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pass type(MyDummy)

#

Any keyword arguments (except metaclass) in the class declaration will be passed to the metaclass. Thus class MyDummy(metaclass=mytype, x=2) will pass x=2 as a keyword argument to the mytype constructor.

Read this in-depth description of python meta-classes for more details.

Section 39.2: Singletons using metaclasses A singleton is a pattern that restricts the instantiation of a class to one instance/object. For more info on python singleton design patterns, see here. class SingletonType(type): def __call__(cls, *args, **kwargs): try: return cls.__instance except AttributeError: cls.__instance = super(SingletonType, cls).__call__(*args, **kwargs) return cls.__instance

Python 2.x Version

≤ 2.7

class MySingleton(object): __metaclass__ = SingletonType

Python 3.x Version

≥ 3.0

class MySingleton(metaclass=SingletonType): pass MySingleton() is MySingleton()

# True, only one instantiation occurs

Section 39.3: Using a metaclass Metaclass syntax Python 2.x Version

≤ 2.7

class MyClass(object): __metaclass__ = SomeMetaclass

Python 3.x Version

≥ 3.0

class MyClass(metaclass=SomeMetaclass): pass

Python 2 and 3 compatibility with six import six class MyClass(six.with_metaclass(SomeMetaclass)): pass

Section 39.4: Introduction to Metaclasses What is a metaclass? In Python, everything is an object: integers, strings, lists, even functions and classes themselves are objects. And every object is an instance of a class. To check the class of an object x, one can call type(x), so:

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>>> type(5)

>>> type(str)

>>> type([1, 2, 3])

>>> class C(object): ... pass ... >>> type(C)

Most classes in python are instances of type. type itself is also a class. Such classes whose instances are also classes are called metaclasses. The Simplest Metaclass OK, so there is already one metaclass in Python: type. Can we create another one? class SimplestMetaclass(type): pass class MyClass(object): __metaclass__ = SimplestMetaclass

That does not add any functionality, but it is a new metaclass, see that MyClass is now an instance of SimplestMetaclass: >>> type(MyClass)

A Metaclass which does Something A metaclass which does something usually overrides type's __new__, to modify some properties of the class to be created, before calling the original __new__ which creates the class: class AnotherMetaclass(type): def __new__(cls, name, parents, dct): # cls is this class # name is the name of the class to be created # parents is the list of the class's parent classes # dct is the list of class's attributes (methods, static variables) # here all of the attributes can be modified before creating the class, e.g. dct['x'] = 8

# now the class will have a static variable x = 8

# return value is the new class. super will take care of that return super(AnotherMetaclass, cls).__new__(cls, name, parents, dct)

Section 39.5: Custom functionality with metaclasses Functionality in metaclasses can be changed so that whenever a class is built, a string is printed to standard output, or an exception is thrown. This metaclass will print the name of the class being built. class VerboseMetaclass(type):

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def __new__(cls, class_name, class_parents, class_dict): print("Creating class ", class_name) new_class = super().__new__(cls, class_name, class_parents, class_dict) return new_class

You can use the metaclass like so: class Spam(metaclass=VerboseMetaclass): def eggs(self): print("[insert example string here]") s = Spam() s.eggs()

The standard output will be: Creating class Spam [insert example string here]

Section 39.6: The default metaclass You may have heard that everything in Python is an object. It is true, and all objects have a class: >>> type(1) int

The literal 1 is an instance of int. Let's declare a class: >>> class Foo(object): ... pass ...

Now let's instantiate it: >>> bar = Foo()

What is the class of bar? >>> type(bar) Foo

Nice, bar is an instance of Foo. But what is the class of Foo itself? >>> type(Foo) type

Ok, Foo itself is an instance of type. How about type itself? >>> type(type) type

So what is a metaclass? For now let's pretend it is just a fancy name for the class of a class. Takeaways: Everything is an object in Python, so everything has a class The class of a class is called a metaclass The default metaclass is type, and by far it is the most common metaclass GoalKicker.com – Python® Notes for Professionals

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But why should you know about metaclasses? Well, Python itself is quite "hackable", and the concept of metaclass is important if you are doing advanced stuff like meta-programming or if you want to control how your classes are initialized.

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Chapter 40: String Formatting When storing and transforming data for humans to see, string formatting can become very important. Python offers a wide variety of string formatting methods which are outlined in this topic.

Section 40.1: Basics of String Formatting foo = 1 bar = 'bar' baz = 3.14

You can use str.format to format output. Bracket pairs are replaced with arguments in the order in which the arguments are passed: print('{}, {} and {}'.format(foo, bar, baz)) # Out: "1, bar and 3.14"

Indexes can also be specified inside the brackets. The numbers correspond to indexes of the arguments passed to the str.format function (0-based). print('{0}, {1}, {2}, and {1}'.format(foo, bar, baz)) # Out: "1, bar, 3.14, and bar" print('{0}, {1}, {2}, and {3}'.format(foo, bar, baz)) # Out: index out of range error

Named arguments can be also used: print("X value is: {x_val}. Y value is: {y_val}.".format(x_val=2, y_val=3)) # Out: "X value is: 2. Y value is: 3."

Object attributes can be referenced when passed into str.format: class AssignValue(object): def __init__(self, value): self.value = value my_value = AssignValue(6) print('My value is: {0.value}'.format(my_value)) # Out: "My value is: 6"

# "0" is optional

Dictionary keys can be used as well: my_dict = {'key': 6, 'other_key': 7} print("My other key is: {0[other_key]}".format(my_dict)) # Out: "My other key is: 7"

# "0" is optional

Same applies to list and tuple indices: my_list = ['zero', 'one', 'two'] print("2nd element is: {0[2]}".format(my_list)) # Out: "2nd element is: two"

# "0" is optional

Note: In addition to str.format, Python also provides the modulo operator %--also known as the string formatting or interpolation operator (see PEP 3101)--for formatting strings. str.format is a successor of % GoalKicker.com – Python® Notes for Professionals

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and it offers greater flexibility, for instance by making it easier to carry out multiple substitutions. In addition to argument indexes, you can also include a format specification inside the curly brackets. This is an expression that follows special rules and must be preceded by a colon (:). See the docs for a full description of format specification. An example of format specification is the alignment directive :~^20 (^ stands for center alignment, total width 20, fill with ~ character): '{:~^20}'.format('centered') # Out: '~~~~~~centered~~~~~~' format allows behaviour not possible with %, for example repetition of arguments: t = (12, 45, 22222, 103, 6) print '{0} {2} {1} {2} {3} {2} {4} {2}'.format(*t) # Out: 12 22222 45 22222 103 22222 6 22222

As format is a function, it can be used as an argument in other functions: number_list = [12,45,78] print map('the number is {}'.format, number_list) # Out: ['the number is 12', 'the number is 45', 'the number is 78']

from datetime import datetime,timedelta once_upon_a_time = datetime(2010, 7, 1, 12, 0, 0) delta = timedelta(days=13, hours=8, minutes=20) gen = (once_upon_a_time + x * delta for x in xrange(5)) print #Out: # # # #

'\n'.join(map('{:%Y-%m-%d %H:%M:%S}'.format, gen)) 2010-07-01 12:00:00 2010-07-14 20:20:00 2010-07-28 04:40:00 2010-08-10 13:00:00 2010-08-23 21:20:00

Section 40.2: Alignment and padding Python 2.x Version

≥ 2.6

The format() method can be used to change the alignment of the string. You have to do it with a format expression of the form :[fill_char][align_operator][width] where align_operator is one of: < forces the field to be left-aligned within width. > forces the field to be right-aligned within width. ^ forces the field to be centered within width. = forces the padding to be placed after the sign (numeric types only). fill_char (if omitted default is whitespace) is the character used for the padding. '{:~9s}, World'.format('Hello') # '~~~~Hello, World'

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'{:~^9s}'.format('Hello') # '~~Hello~~' '{:0=6d}'.format(-123) # '-00123'

Note: you could achieve the same results using the string functions ljust(), rjust(), center(), zfill(), however these functions are deprecated since version 2.5.

Section 40.3: Format literals (f-string) Literal format strings were introduced in PEP 498 (Python3.6 and upwards), allowing you to prepend f to the beginning of a string literal to effectively apply .format to it with all variables in the current scope. >>> foo = 'bar' >>> f'Foo is {foo}' 'Foo is bar'

This works with more advanced format strings too, including alignment and dot notation. >>> f'{foo:^7s}' ' bar '

Note: The f'' does not denote a particular type like b'' for bytes or u'' for unicode in python2. The formatting is immediately applied, resulting in a normal string. The format strings can also be nested: >>> price = 478.23 >>> f"{f'${price:0.2f}':*>20s}" '*************$478.23'

The expressions in an f-string are evaluated in left-to-right order. This is detectable only if the expressions have side effects: >>> def fn(l, incr): ... result = l[0] ... l[0] += incr ... return result ... >>> lst = [0] >>> f'{fn(lst,2)} {fn(lst,3)}' '0 2' >>> f'{fn(lst,2)} {fn(lst,3)}' '5 7' >>> lst [10]

Section 40.4: Float formatting >>> '{0:.0f}'.format(42.12345) '42' >>> '{0:.1f}'.format(42.12345) '42.1' >>> '{0:.3f}'.format(42.12345)

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'42.123' >>> '{0:.5f}'.format(42.12345) '42.12345' >>> '{0:.7f}'.format(42.12345) '42.1234500'

Same hold for other way of referencing: >>> '{:.3f}'.format(42.12345) '42.123' >>> '{answer:.3f}'.format(answer=42.12345) '42.123'

Floating point numbers can also be formatted in scientific notation or as percentages: >>> '{0:.3e}'.format(42.12345) '4.212e+01' >>> '{0:.0%}'.format(42.12345) '4212%'

You can also combine the {0} and {name} notations. This is especially useful when you want to round all variables to a pre-specified number of decimals with 1 declaration: >>> s = 'Hello' >>> a, b, c = 1.12345, 2.34567, 34.5678 >>> digits = 2 >>> '{0}! {1:.{n}f}, {2:.{n}f}, {3:.{n}f}'.format(s, a, b, c, n=digits) 'Hello! 1.12, 2.35, 34.57'

Section 40.5: Named placeholders Format strings may contain named placeholders that are interpolated using keyword arguments to format. Using a dictionary (Python 2.x) >>> data = {'first': 'Hodor', 'last': 'Hodor!'} >>> '{first} {last}'.format(**data) 'Hodor Hodor!'

Using a dictionary (Python 3.2+) >>> '{first} {last}'.format_map(data) 'Hodor Hodor!' str.format_map allows to use dictionaries without having to unpack them first. Also the class of data (which might

be a custom type) is used instead of a newly filled dict. Without a dictionary: >>> '{first} {last}'.format(first='Hodor', last='Hodor!') 'Hodor Hodor!'

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Section 40.6: String formatting with datetime Any class can configure its own string formatting syntax through the __format__ method. A type in the standard Python library that makes handy use of this is the datetime type, where one can use strftime-like formatting codes directly within str.format: >>> from datetime import datetime >>> 'North America: {dt:%m/%d/%Y}. ISO: {dt:%Y-%m-%d}.'.format(dt=datetime.now()) 'North America: 07/21/2016. ISO: 2016-07-21.'

A full list of list of datetime formatters can be found in the official documentation.

Section 40.7: Formatting Numerical Values The .format() method can interpret a number in different formats, such as: >>> '{:c}'.format(65) 'A'

# Unicode character

>>> '{:d}'.format(0x0a) '10'

# base 10

>>> '{:n}'.format(0x0a) '10'

# base 10 using current locale for separators

Format integers to different bases (hex, oct, binary) >>> '{0:x}'.format(10) # base 16, lowercase - Hexadecimal 'a' >>> '{0:X}'.format(10) # base 16, uppercase - Hexadecimal 'A' >>> '{:o}'.format(10) # base 8 - Octal '12' >>> '{:b}'.format(10) # base 2 - Binary '1010' >>> '{0:#b}, {0:#o}, {0:#x}'.format(42) # With prefix '0b101010, 0o52, 0x2a' >>> '8 bit: {0:08b}; Three bytes: {0:06x}'.format(42) # Add zero padding '8 bit: 00101010; Three bytes: 00002a'

Use formatting to convert an RGB float tuple to a color hex string: >>> r, g, b = (1.0, 0.4, 0.0) >>> '#{:02X}{:02X}{:02X}'.format(int(255 * r), int(255 * g), int(255 * b)) '#FF6600'

Only integers can be converted: >>> '{:x}'.format(42.0) Traceback (most recent call last): File "", line 1, in ValueError: Unknown format code 'x' for object of type 'float'

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Section 40.8: Nested formatting Some formats can take additional parameters, such as the width of the formatted string, or the alignment: >>> '{:.>10}'.format('foo') '.......foo'

Those can also be provided as parameters to format by nesting more {} inside the {}: >>> '{:.>{}}'.format('foo', 10) '.......foo' '{:{}{}{}}'.format('foo', '*', '^', 15) '******foo******'

In the latter example, the format string '{:{}{}{}}' is modified to '{:*^15}' (i.e. "center and pad with * to total length of 15") before applying it to the actual string 'foo' to be formatted that way. This can be useful in cases when parameters are not known beforehand, for instances when aligning tabular data: >>> data = ["a", "bbbbbbb", "ccc"] >>> m = max(map(len, data)) >>> for d in data: ... print('{:>{}}'.format(d, m)) a bbbbbbb ccc

Section 40.9: Format using Getitem and Getattr Any data structure that supports __getitem__ can have their nested structure formatted: person = {'first': 'Arthur', 'last': 'Dent'} '{p[first]} {p[last]}'.format(p=person) # 'Arthur Dent'

Object attributes can be accessed using getattr(): class Person(object): first = 'Zaphod' last = 'Beeblebrox' '{p.first} {p.last}'.format(p=Person()) # 'Zaphod Beeblebrox'

Section 40.10: Padding and truncating strings, combined Say you want to print variables in a 3 character column. Note: doubling { and } escapes them. s = """ pad {{:3}}

:{a:3}:

truncate

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{{:.3}}

:{e:.3}:

combined {{:>3.3}} {{:3.3}} {{:3.3}} {{:3.3}} """

:{a:>3.3}: :{a:3.3}: :{c:3.3}: :{e:3.3}:

print (s.format(a="1"*1, c="3"*3, e="5"*5))

Output: pad {:3}

:1

truncate {:.3}

:555:

combined {:>3.3} {:3.3} {:3.3} {:3.3}

: 1: :1 : :333: :555:

:

Section 40.11: Custom formatting for a class Note: Everything below applies to the str.format method, as well as the format function. In the text below, the two are interchangeable. For every value which is passed to the format function, Python looks for a __format__ method for that argument. Your own custom class can therefore have their own __format__ method to determine how the format function will display and format your class and it's attributes. This is different than the __str__ method, as in the __format__ method you can take into account the formatting language, including alignment, field width etc, and even (if you wish) implement your own format specifiers, and your own formatting language extensions.1 object.__format__(self, format_spec)

For example: # Example in Python 2 - but can be easily applied to Python 3 class Example(object): def __init__(self,a,b,c): self.a, self.b, self.c = a,b,c def __format__(self, format_spec): """ Implement special semantics for the 's' format specifier """ # Reject anything that isn't an s if format_spec[-1] != 's': raise ValueError('{} format specifier not understood for this object', format_spec[:-1])

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# Output in this example will be (,,) raw = "(" + ",".join([str(self.a), str(self.b), # Honor the format language by using the inbuilt # Since we know the original format_spec ends in # we can take advantage of the str.format method # string argument we constructed above return "{r:{f}}".format( r=raw, f=format_spec )

str(self.c)]) + ")" string format an 's' with a

inst = Example(1,2,3) print "{0:>20s}".format( inst ) # out : (1,2,3) # Note how the right align and field width of 20 has been honored.

Note: If your custom class does not have a custom __format__ method and an instance of the class is passed to the format function, Python2 will always use the return value of the __str__ method or __repr__ method to determine what to print (and if neither exist then the default repr will be used), and you will need to use the s format specifier to format this. With Python3, to pass your custom class to the format function, you will need define __format__ method on your custom class.

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Chapter 41: String Methods Section 41.1: Changing the capitalization of a string Python's string type provides many functions that act on the capitalization of a string. These include: str.casefold str.upper str.lower str.capitalize str.title str.swapcase

With unicode strings (the default in Python 3), these operations are not 1:1 mappings or reversible. Most of these operations are intended for display purposes, rather than normalization.

Python 3.x Version

≥ 3.3

str.casefold() str.casefold creates a lowercase string that is suitable for case insensitive comparisons. This is more aggressive

than str.lower and may modify strings that are already in lowercase or cause strings to grow in length, and is not intended for display purposes. "XßΣ".casefold() # 'xssσ' "XßΣ".lower() # 'xßς'

The transformations that take place under casefolding are defined by the Unicode Consortium in the CaseFolding.txt file on their website.

str.upper() str.upper takes every character in a string and converts it to its uppercase equivalent, for example: "This is a 'string'.".upper() # "THIS IS A 'STRING'." str.lower() str.lower does the opposite; it takes every character in a string and converts it to its lowercase equivalent: "This IS a 'string'.".lower() # "this is a 'string'." str.capitalize() str.capitalize returns a capitalized version of the string, that is, it makes the first character have upper case and

the rest lower: "this Is A 'String'.".capitalize() # Capitalizes the first character and lowercases all others

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# "This is a 'string'." str.title() str.title returns the title cased version of the string, that is, every letter in the beginning of a word is made upper

case and all others are made lower case: "this Is a 'String'".title() # "This Is A 'String'" str.swapcase() str.swapcase returns a new string object in which all lower case characters are swapped to upper case and all

upper case characters to lower: "this iS A STRiNG".swapcase() #Swaps case of each character # "THIS Is a strIng"

Usage as str class methods It is worth noting that these methods may be called either on string objects (as shown above) or as a class method of the str class (with an explicit call to str.upper, etc.) str.upper("This is a 'string'") # "THIS IS A 'STRING'"

This is most useful when applying one of these methods to many strings at once in say, a map function. map(str.upper,["These","are","some","'strings'"]) # ['THESE', 'ARE', 'SOME', "'STRINGS'"]

Section 41.2: str.translate: Translating characters in a string Python supports a translate method on the str type which allows you to specify the translation table (used for replacements) as well as any characters which should be deleted in the process. str.translate(table[, deletechars]) Parameter Description table It is a lookup table that defines the mapping from one character to another. deletechars A list of characters which are to be removed from the string.

The maketrans method (str.maketrans in Python 3 and string.maketrans in Python 2) allows you to generate a translation table. >>> translation_table = str.maketrans("aeiou", "12345") >>> my_string = "This is a string!" >>> translated = my_string.translate(translation_table) 'Th3s 3s 1 str3ng!'

The translate method returns a string which is a translated copy of the original string. You can set the table argument to None if you only need to delete characters. >>> 'this syntax is very useful'.translate(None, 'aeiou')

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'ths syntx s vry sfl'

Section 41.3: str.format and f-strings: Format values into a string Python provides string interpolation and formatting functionality through the str.format function, introduced in version 2.6 and f-strings introduced in version 3.6. Given the following variables: i f s l d

= = = = =

10 1.5 "foo" ['a', 1, 2] {'a': 1, 2: 'foo'}

The following statements are all equivalent "10 1.5 foo ['a', 1, 2] {'a': 1, 2: 'foo'}" >>> "{} {} {} {} {}".format(i, f, s, l, d) >>> str.format("{} {} {} {} {}", i, f, s, l, d) >>> "{0} {1} {2} {3} {4}".format(i, f, s, l, d) >>> "{0:d} {1:0.1f} {2} {3!r} {4!r}".format(i, f, s, l, d) >>> "{i:d} {f:0.1f} {s} {l!r} {d!r}".format(i=i, f=f, s=s, l=l, d=d) >>> f"{i} {f} {s} {l} {d}" >>> f"{i:d} {f:0.1f} {s} {l!r} {d!r}"

For reference, Python also supports C-style qualifiers for string formatting. The examples below are equivalent to those above, but the str.format versions are preferred due to benefits in flexibility, consistency of notation, and extensibility: "%d %0.1f %s %r %r" % (i, f, s, l, d) "%(i)d %(f)0.1f %(s)s %(l)r %(d)r" % dict(i=i, f=f, s=s, l=l, d=d)

The braces uses for interpolation in str.format can also be numbered to reduce duplication when formatting strings. For example, the following are equivalent: "I am from Australia. I love cupcakes from Australia!" >>> "I am from {}. I love cupcakes from {}!".format("Australia", "Australia") >>> "I am from {0}. I love cupcakes from {0}!".format("Australia")

While the official python documentation is, as usual, thorough enough, pyformat.info has a great set of examples with detailed explanations. Additionally, the { and } characters can be escaped by using double brackets: "{'a': 5, 'b': 6}"

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>>> "{{'{}': {}, '{}': {}}}".format("a", 5, "b", 6) >>> f"{{'{'a'}': {5}, '{'b'}': {6}}"

See String Formatting for additional information. str.format() was proposed in PEP 3101 and f-strings in PEP 498.

Section 41.4: String module's useful constants Python's string module provides constants for string related operations. To use them, import the string module: >>> import string string.ascii_letters:

Concatenation of ascii_lowercase and ascii_uppercase: >>> string.ascii_letters 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ' string.ascii_lowercase:

Contains all lower case ASCII characters: >>> string.ascii_lowercase 'abcdefghijklmnopqrstuvwxyz' string.ascii_uppercase:

Contains all upper case ASCII characters: >>> string.ascii_uppercase 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' string.digits:

Contains all decimal digit characters: >>> string.digits '0123456789' string.hexdigits:

Contains all hex digit characters: >>> string.hexdigits '0123456789abcdefABCDEF' string.octaldigits:

Contains all octal digit characters: >>> string.octaldigits '01234567'

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string.punctuation:

Contains all characters which are considered punctuation in the C locale: >>> string.punctuation '!"#$%&\'()*+,-./:;?@[\\]^_`{|}~' string.whitespace:

Contains all ASCII characters considered whitespace: >>> string.whitespace ' \t\n\r\x0b\x0c'

In script mode, print(string.whitespace) will print the actual characters, use str to get the string returned above.

string.printable:

Contains all characters which are considered printable; a combination of string.digits, string.ascii_letters, string.punctuation, and string.whitespace. >>> string.printable '0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&\'()*+,-./:;?@[\\]^_`{|}~ \t\n\r\x0b\x0c'

Section 41.5: Stripping unwanted leading/trailing characters from a string Three methods are provided that offer the ability to strip leading and trailing characters from a string: str.strip, str.rstrip and str.lstrip. All three methods have the same signature and all three return a new string object

with unwanted characters removed.

str.strip([chars]) str.strip acts on a given string and removes (strips) any leading or trailing characters contained in the argument chars; if chars is not supplied or is None, all white space characters are removed by default. For example: >>> " a line with leading and trailing space 'a line with leading and trailing space'

".strip()

If chars is supplied, all characters contained in it are removed from the string, which is returned. For example: >>> ">>> a Python prompt".strip('> ') 'a Python prompt'

# strips '>' character and space character

str.rstrip([chars]) and str.lstrip([chars])

These methods have similar semantics and arguments with str.strip(), their difference lies in the direction from which they start. str.rstrip() starts from the end of the string while str.lstrip() splits from the start of the string.

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For example, using str.rstrip: >>> " spacious string ' spacious string'

".rstrip()

While, using str.lstrip: >>> " spacious string 'spacious string '

".rstrip()

Section 41.6: Reversing a string A string can reversed using the built-in reversed() function, which takes a string and returns an iterator in reverse order. >>> reversed('hello')

>>> [char for char in reversed('hello')] ['o', 'l', 'l', 'e', 'h'] reversed() can be wrapped in a call to ''.join() to make a string from the iterator. >>> ''.join(reversed('hello')) 'olleh'

While using reversed() might be more readable to uninitiated Python users, using extended slicing with a step of -1 is faster and more concise. Here , try to implement it as function: >>> def reversed_string(main_string): ... return main_string[::-1] ... >>> reversed_string('hello') 'olleh'

Section 41.7: Split a string based on a delimiter into a list of strings str.split(sep=None, maxsplit=-1) str.split takes a string and returns a list of substrings of the original string. The behavior differs depending on

whether the sep argument is provided or omitted. If sep isn't provided, or is None, then the splitting takes place wherever there is whitespace. However, leading and trailing whitespace is ignored, and multiple consecutive whitespace characters are treated the same as a single whitespace character: >>> "This is a sentence.".split() ['This', 'is', 'a', 'sentence.'] >>> " This is a sentence. ".split() ['This', 'is', 'a', 'sentence.'] >>> " []

".split()

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The sep parameter can be used to define a delimiter string. The original string is split where the delimiter string occurs, and the delimiter itself is discarded. Multiple consecutive delimiters are not treated the same as a single occurrence, but rather cause empty strings to be created. >>> "This is a sentence.".split(' ') ['This', 'is', 'a', 'sentence.'] >>> "Earth,Stars,Sun,Moon".split(',') ['Earth', 'Stars', 'Sun', 'Moon'] >>> " This is a sentence. ".split(' ') ['', 'This', 'is', '', '', '', 'a', 'sentence.', '', ''] >>> "This is a sentence.".split('e') ['This is a s', 'nt', 'nc', '.'] >>> "This is a sentence.".split('en') ['This is a s', 't', 'ce.']

The default is to split on every occurrence of the delimiter, however the maxsplit parameter limits the number of splittings that occur. The default value of -1 means no limit: >>> "This is a sentence.".split('e', maxsplit=0) ['This is a sentence.'] >>> "This is a sentence.".split('e', maxsplit=1) ['This is a s', 'ntence.'] >>> "This is a sentence.".split('e', maxsplit=2) ['This is a s', 'nt', 'nce.'] >>> "This is a sentence.".split('e', maxsplit=-1) ['This is a s', 'nt', 'nc', '.'] str.rsplit(sep=None, maxsplit=-1) str.rsplit ("right split") differs from str.split ("left split") when maxsplit is specified. The splitting starts at the

end of the string rather than at the beginning: >>> "This is a sentence.".rsplit('e', maxsplit=1) ['This is a sentenc', '.'] >>> "This is a sentence.".rsplit('e', maxsplit=2) ['This is a sent', 'nc', '.']

Note: Python specifies the maximum number of splits performed, while most other programming languages specify the maximum number of substrings created. This may create confusion when porting or comparing code.

Section 41.8: Replace all occurrences of one substring with another substring Python's str type also has a method for replacing occurrences of one sub-string with another sub-string in a given string. For more demanding cases, one can use re.sub.

str.replace(old, new[, count]):

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str.replace takes two arguments old and new containing the old sub-string which is to be replaced by the new sub-

string. The optional argument count specifies the number of replacements to be made: For example, in order to replace 'foo' with 'spam' in the following string, we can call str.replace with old = 'foo' and new = 'spam': >>> "Make sure to foo your sentence.".replace('foo', 'spam') "Make sure to spam your sentence."

If the given string contains multiple examples that match the old argument, all occurrences are replaced with the value supplied in new: >>> "It can foo multiple examples of foo if you want.".replace('foo', 'spam') "It can spam multiple examples of spam if you want."

unless, of course, we supply a value for count. In this case count occurrences are going to get replaced: >>> """It can foo multiple examples of foo if you want, \ ... or you can limit the foo with the third argument.""".replace('foo', 'spam', 1) 'It can spam multiple examples of foo if you want, or you can limit the foo with the third argument.'

Section 41.9: Testing what a string is composed of Python's str type also features a number of methods that can be used to evaluate the contents of a string. These are str.isalpha, str.isdigit, str.isalnum, str.isspace. Capitalization can be tested with str.isupper, str.islower and str.istitle.

str.isalpha str.isalpha takes no arguments and returns True if the all characters in a given string are alphabetic, for example: >>> "Hello World".isalpha() False >>> "Hello2World".isalpha() False >>> "HelloWorld!".isalpha() False >>> "HelloWorld".isalpha() True

# contains a space # contains a number # contains punctuation

As an edge case, the empty string evaluates to False when used with "".isalpha().

str.isupper, str.islower, str.istitle

These methods test the capitalization in a given string. str.isupper is a method that returns True if all characters in a given string are uppercase and False otherwise. >>> "HeLLO WORLD".isupper() False >>> "HELLO WORLD".isupper() True >>> "".isupper()

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False

Conversely, str.islower is a method that returns True if all characters in a given string are lowercase and False otherwise. >>> "Hello world".islower() False >>> "hello world".islower() True >>> "".islower() False str.istitle returns True if the given string is title cased; that is, every word begins with an uppercase character

followed by lowercase characters. >>> "hello world".istitle() False >>> "Hello world".istitle() False >>> "Hello World".istitle() True >>> "".istitle() False str.isdecimal, str.isdigit, str.isnumeric str.isdecimal returns whether the string is a sequence of decimal digits, suitable for representing a decimal

number. str.isdigit includes digits not in a form suitable for representing a decimal number, such as superscript digits. str.isnumeric includes any number values, even if not digits, such as values outside the range 0-9. isdecimal 12345 ?2??5 ?²³????? ?? Five

True True False False False

isdigit True True True False False

isnumeric True True True True False

Bytestrings (bytes in Python 3, str in Python 2), only support isdigit, which only checks for basic ASCII digits. As with str.isalpha, the empty string evaluates to False.

str.isalnum

This is a combination of str.isalpha and str.isnumeric, specifically it evaluates to True if all characters in the given string are alphanumeric, that is, they consist of alphabetic or numeric characters: >>> "Hello2World".isalnum() True >>> "HelloWorld".isalnum() True >>> "2016".isalnum() True

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>>> "Hello World".isalnum() False

# contains whitespace

str.isspace

Evaluates to True if the string contains only whitespace characters. >>> "\t\r\n".isspace() True >>> " ".isspace() True

Sometimes a string looks “empty” but we don't know whether it's because it contains just whitespace or no character at all >>> "".isspace() False

To cover this case we need an additional test >>> my_str = '' >>> my_str.isspace() False >>> my_str.isspace() or not my_str True

But the shortest way to test if a string is empty or just contains whitespace characters is to use strip(with no arguments it removes all leading and trailing whitespace characters) >>> not my_str.strip() True

Section 41.10: String Contains Python makes it extremely intuitive to check if a string contains a given substring. Just use the in operator: >>> "foo" in "foo.baz.bar" True

Note: testing an empty string will always result in True: >>> "" in "test" True

Section 41.11: Join a list of strings into one string A string can be used as a separator to join a list of strings together into a single string using the join() method. For example you can create a string where each element in a list is separated by a space. >>> " ".join(["once","upon","a","time"]) "once upon a time"

The following example separates the string elements with three hyphens. >>> "---".join(["once", "upon", "a", "time"])

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"once---upon---a---time"

Section 41.12: Counting number of times a substring appears in a string One method is available for counting the number of occurrences of a sub-string in another string, str.count.

str.count(sub[, start[, end]]) str.count returns an int indicating the number of non-overlapping occurrences of the sub-string sub in another

string. The optional arguments start and end indicate the beginning and the end in which the search will take place. By default start = 0 and end = len(str) meaning the whole string will be searched: >>> >>> 2 >>> 3 >>> 2 >>> 1

s = "She sells seashells by the seashore." s.count("sh") s.count("se") s.count("sea") s.count("seashells")

By specifying a different value for start, end we can get a more localized search and count, for example, if start is equal to 13 the call to: >>> s.count("sea", start) 1

is equivalent to: >>> t = s[start:] >>> t.count("sea") 1

Section 41.13: Case insensitive string comparisons Comparing string in a case insensitive way seems like something that's trivial, but it's not. This section only considers unicode strings (the default in Python 3). Note that Python 2 may have subtle weaknesses relative to Python 3 - the later's unicode handling is much more complete. The first thing to note it that case-removing conversions in unicode aren't trivial. There is text for which text.lower() != text.upper().lower(), such as "ß": >>> "ß".lower() 'ß' >>> "ß".upper().lower() 'ss'

But let's say you wanted to caselessly compare "BUSSE" and "Buße". You probably also want to compare "BUSSE" and "BU E" equal - that's the newer capital form. The recommended way is to use casefold: Python 3.x Version

≥ 3.3

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>>> help(str.casefold) """ Help on method_descriptor: casefold(...) S.casefold() -> str Return a version of S suitable for caseless comparisons. """

Do not just use lower. If casefold is not available, doing .upper().lower() helps (but only somewhat). Then you should consider accents. If your font renderer is good, you probably think "ê" == "ê" - but it doesn't: >>> "ê" == "ê" False

This is because they are actually >>> import unicodedata >>> [unicodedata.name(char) for char in "ê"] ['LATIN SMALL LETTER E WITH CIRCUMFLEX'] >>> [unicodedata.name(char) for char in "ê"] ['LATIN SMALL LETTER E', 'COMBINING CIRCUMFLEX ACCENT']

The simplest way to deal with this is unicodedata.normalize. You probably want to use NFKD normalization, but feel free to check the documentation. Then one does >>> unicodedata.normalize("NFKD", "ê") == unicodedata.normalize("NFKD", "ê") True

To finish up, here this is expressed in functions: import unicodedata def normalize_caseless(text): return unicodedata.normalize("NFKD", text.casefold()) def caseless_equal(left, right): return normalize_caseless(left) == normalize_caseless(right)

Section 41.14: Justify strings Python provides functions for justifying strings, enabling text padding to make aligning various strings much easier. Below is an example of str.ljust and str.rjust: interstates_lengths = { 5: (1381, 2222), 19: (63, 102), 40: (2555, 4112), 93: (189,305), } for road, length in interstates_lengths.items(): miles,kms = length

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print('{} -> {} mi. ({} km.)'.format(str(road).rjust(4), str(miles).ljust(4), str(kms).ljust(4))) 40 19 5 93

-> -> -> ->

2555 63 1381 189

mi. mi. mi. mi.

(4112 (102 (2222 (305

km.) km.) km.) km.)

ljust and rjust are very similar. Both have a width parameter and an optional fillchar parameter. Any string

created by these functions is at least as long as the width parameter that was passed into the function. If the string is longer than width alread, it is not truncated. The fillchar argument, which defaults to the space character ' ' must be a single character, not a multicharacter string. The ljust function pads the end of the string it is called on with the fillchar until it is width characters long. The rjust function pads the beginning of the string in a similar fashion. Therefore, the l and r in the names of these

functions refer to the side that the original string, not the fillchar, is positioned in the output string.

Section 41.15: Test the starting and ending characters of a string In order to test the beginning and ending of a given string in Python, one can use the methods str.startswith() and str.endswith().

str.startswith(prefix[, start[, end]])

As its name implies, str.startswith is used to test whether a given string starts with the given characters in prefix. >>> s = "This is a test string" >>> s.startswith("T") True >>> s.startswith("Thi") True >>> s.startswith("thi") False

The optional arguments start and end specify the start and end points from which the testing will start and finish. In the following example, by specifying a start value of 2 our string will be searched from position 2 and afterwards: >>> s.startswith("is", 2) True

This yields True since s[2] == 'i' and s[3] == 's'. You can also use a tuple to check if it starts with any of a set of strings >>> s.startswith(('This', 'That')) True >>> s.startswith(('ab', 'bc')) False str.endswith(prefix[, start[, end]]) str.endswith is exactly similar to str.startswith with the only difference being that it searches for ending

characters and not starting characters. For example, to test if a string ends in a full stop, one could write: GoalKicker.com – Python® Notes for Professionals

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>>> s = "this ends in a full stop." >>> s.endswith('.') True >>> s.endswith('!') False

as with startswith more than one characters can used as the ending sequence: >>> s.endswith('stop.') True >>> s.endswith('Stop.') False

You can also use a tuple to check if it ends with any of a set of strings >>> s.endswith(('.', 'something')) True >>> s.endswith(('ab', 'bc')) False

Section 41.16: Conversion between str or bytes data and unicode characters The contents of files and network messages may represent encoded characters. They often need to be converted to unicode for proper display. In Python 2, you may need to convert str data to Unicode characters. The default ('', "", etc.) is an ASCII string, with any values outside of ASCII range displayed as escaped values. Unicode strings are u'' (or u"", etc.). Python 2.x Version

≥ 2.3

# You get "© abc" encoded in UTF-8 from a file, network, or other data source s = '\xc2\xa9 abc'

s[0] type(s)

# # # # #

u = s.decode('utf-8')

s is a byte array, not a string of characters Doesn't know the original was UTF-8 Default form of string literals in Python 2 '\xc2' - meaningless byte (without context such as an encoding) str - even though it's not a useful one w/o having a known encoding # u'\xa9 abc' # Now we have a Unicode string, which can be read as UTF-8 and printed

properly # In Python 2, Unicode string literals need a leading u # str.decode converts a string which may contain escaped bytes to a Unicode string u[0] type(u)

# u'\xa9' - Unicode Character 'COPYRIGHT SIGN' (U+00A9) '©' # unicode

u.encode('utf-8')

# '\xc2\xa9 abc' # unicode.encode produces a string with escaped bytes for non-ASCII characters

In Python 3 you may need to convert arrays of bytes (referred to as a 'byte literal') to strings of Unicode characters. The default is now a Unicode string, and bytestring literals must now be entered as b'', b"", etc. A byte literal will return True to isinstance(some_val, byte), assuming some_val to be a string that might be encoded as bytes. Python 3.x Version

≥ 3.0

# You get from file or network "© abc" encoded in UTF-8

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s = b'\xc2\xa9 abc' # # leading b s[0] # type(s) #

s is a byte array, not characters In Python 3, the default string literal is Unicode; byte array literals need a

u = s.decode('utf-8')

# '© abc' on a Unicode terminal # bytes.decode converts a byte array to a string (which will, in Python 3, be

Unicode) u[0] type(u)

u.encode('utf-8')

b'\xc2' - meaningless byte (without context such as an encoding) bytes - now that byte arrays are explicit, Python can show that.

# '\u00a9' - Unicode Character 'COPYRIGHT SIGN' (U+00A9) '©' # str # The default string literal in Python 3 is UTF-8 Unicode # b'\xc2\xa9 abc' # str.encode produces a byte array, showing ASCII-range bytes as unescaped

characters.

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Chapter 42: Using loops within functions In Python function will be returned as soon as execution hits "return" statement.

Section 42.1: Return statement inside loop in a function In this example, function will return as soon as value var has 1 def func(params): for value in params: print ('Got value {}'.format(value)) if value == 1: # Returns from function as soon as value is 1 print (">>>> Got 1") return print ("Still looping") return "Couldn't find 1" func([5, 3, 1, 2, 8, 9])

output Got value 5 Still looping Got value 3 Still looping Got value 1 >>>> Got 1

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Chapter 43: Importing modules Section 43.1: Importing a module Use the import statement: >>> import random >>> print(random.randint(1, 10)) 4 import module will import a module and then allow you to reference its objects -- values, functions and classes, for

example -- using the module.name syntax. In the above example, the random module is imported, which contains the randint function. So by importing random you can call randint with random.randint.

You can import a module and assign it to a different name: >>> import random as rn >>> print(rn.randint(1, 10)) 4

If your python file main.py is in the same folder as custom.py. You can import it like this: import custom

It is also possible to import a function from a module: >>> from math import sin >>> sin(1) 0.8414709848078965

To import specific functions deeper down into a module, the dot operator may be used only on the left side of the import keyword: from urllib.request import urlopen

In python, we have two ways to call function from top level. One is import and another is from. We should use import when we have a possibility of name collision. Suppose we have hello.py file and world.py files having same

function named function. Then import statement will work good. from hello import function from world import function function() #world's function will be invoked. Not hello's

In general import will provide you a namespace. import hello import world hello.function() # exclusively hello's function will be invoked world.function() # exclusively world's function will be invoked

But if you are sure enough, in your whole project there is no way having same function name you should use from statement GoalKicker.com – Python® Notes for Professionals

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Multiple imports can be made on the same line: >>> >>> >>> >>> >>> >>>

# Multiple modules import time, sockets, random # Multiple functions from math import sin, cos, tan # Multiple constants from math import pi, e

>>> print(pi) 3.141592653589793 >>> print(cos(45)) 0.5253219888177297 >>> print(time.time()) 1482807222.7240417

The keywords and syntax shown above can also be used in combinations: >>> from urllib.request import urlopen as geturl, pathname2url as path2url, getproxies >>> from math import factorial as fact, gamma, atan as arctan >>> import random.randint, time, sys >>> print(time.time()) 1482807222.7240417 >>> print(arctan(60)) 1.554131203080956 >>> filepath = "/dogs/jumping poodle (december).png" >>> print(path2url(filepath)) /dogs/jumping%20poodle%20%28december%29.png

Section 43.2: The __all__ special variable Modules can have a special variable named __all__ to restrict what variables are imported when using from mymodule import *.

Given the following module: # mymodule.py __all__ = ['imported_by_star'] imported_by_star = 42 not_imported_by_star = 21

Only imported_by_star is imported when using from mymodule import *: >>> from mymodule import * >>> imported_by_star 42 >>> not_imported_by_star Traceback (most recent call last): File "", line 1, in NameError: name 'not_imported_by_star' is not defined

However, not_imported_by_star can be imported explicitly: >>> from mymodule import not_imported_by_star >>> not_imported_by_star

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21

Section 43.3: Import modules from an arbitrary filesystem location If you want to import a module that doesn't already exist as a built-in module in the Python Standard Library nor as a side-package, you can do this by adding the path to the directory where your module is found to sys.path. This may be useful where multiple python environments exist on a host. import sys sys.path.append("/path/to/directory/containing/your/module") import mymodule

It is important that you append the path to the directory in which mymodule is found, not the path to the module itself.

Section 43.4: Importing all names from a module from module_name import *

for example: from math import * sqrt(2) # instead of math.sqrt(2) ceil(2.7) # instead of math.ceil(2.7)

This will import all names defined in the math module into the global namespace, other than names that begin with an underscore (which indicates that the writer feels that it is for internal use only). Warning: If a function with the same name was already defined or imported, it will be overwritten. Almost always importing only specific names from math import sqrt, ceil is the recommended way: def sqrt(num): print("I don't know what's the square root of {}.".format(num)) sqrt(4) # Output: I don't know what's the square root of 4. from math import * sqrt(4) # Output: 2.0

Starred imports are only allowed at the module level. Attempts to perform them in class or function definitions result in a SyntaxError. def f(): from math import *

and class A: from math import *

both fail with:

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SyntaxError: import * only allowed at module level

Section 43.5: Programmatic importing Python 2.x Version

≥ 2.7

To import a module through a function call, use the importlib module (included in Python starting in version 2.7): import importlib random = importlib.import_module("random")

The importlib.import_module() function will also import the submodule of a package directly: collections_abc = importlib.import_module("collections.abc")

For older versions of Python, use the imp module. Python 2.x Version

≤ 2.7

Use the functions imp.find_module and imp.load_module to perform a programmatic import. Taken from standard library documentation import imp, sys def import_module(name): fp, pathname, description = imp.find_module(name) try: return imp.load_module(name, fp, pathname, description) finally: if fp: fp.close()

Do NOT use __import__() to programmatically import modules! There are subtle details involving sys.modules, the fromlist argument, etc. that are easy to overlook which importlib.import_module() handles for you.

Section 43.6: PEP8 rules for Imports Some recommended PEP8 style guidelines for imports: 1. Imports should be on separate lines: from math import sqrt, ceil from math import sqrt from math import ceil

# Not recommended # Recommended

2. Order imports as follows at the top of the module: Standard library imports Related third party imports Local application/library specific imports 3. Wildcard imports should be avoided as it leads to confusion in names in the current namespace. If you do from module import *, it can be unclear if a specific name in your code comes from module or not. This is

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doubly true if you have multiple from module import *-type statements. 4. Avoid using relative imports; use explicit imports instead.

Section 43.7: Importing specific names from a module Instead of importing the complete module you can import only specified names: from random import randint # Syntax "from MODULENAME import NAME1[, NAME2[, ...]]" print(randint(1, 10)) # Out: 5 from random is needed, because the python interpreter has to know from which resource it should import a

function or class and import randint specifies the function or class itself. Another example below (similar to the one above): from math import pi print(pi)

# Out: 3.14159265359

The following example will raise an error, because we haven't imported a module: random.randrange(1, 10)

# works only if "import random" has been run before

Outputs: NameError: name 'random' is not defined

The python interpreter does not understand what you mean with random. It needs to be declared by adding import random to the example: import random random.randrange(1, 10)

Section 43.8: Importing submodules from module.submodule import function

This imports function from module.submodule.

Section 43.9: Re-importing a module When using the interactive interpreter, you might want to reload a module. This can be useful if you're editing a module and want to import the newest version, or if you've monkey-patched an element of an existing module and want to revert your changes. Note that you can't just import the module again to revert: import math math.pi = 3 print(math.pi) import math print(math.pi)

# 3 # 3

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This is because the interpreter registers every module you import. And when you try to reimport a module, the interpreter sees it in the register and does nothing. So the hard way to reimport is to use import after removing the corresponding item from the register: print(math.pi) # 3 import sys if 'math' in sys.modules: # Is the ``math`` module in the register? del sys.modules['math'] # If so, remove it. import math print(math.pi) # 3.141592653589793

But there is more a straightforward and simple way. Python 2 Use the reload function: Python 2.x Version import math math.pi = 3 print(math.pi) reload(math) print(math.pi)

≥ 2.3

# 3 # 3.141592653589793

Python 3 The reload function has moved to importlib: Python 3.x Version

≥ 3.0

import math math.pi = 3 print(math.pi) # 3 from importlib import reload reload(math) print(math.pi) # 3.141592653589793

Section 43.10: __import__() function The __import__() function can be used to import modules where the name is only known at runtime if user_input == "os": os = __import__("os") # equivalent to import os

This function can also be used to specify the file path to a module mod = __import__(r"C:/path/to/file/anywhere/on/computer/module.py")

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Chapter 44: Dierence between Module and Package Section 44.1: Modules A module is a single Python file that can be imported. Using a module looks like this: module.py def hi(): print("Hello world!") my_script.py import module module.hi()

in an interpreter >>> from module import hi >>> hi() # Hello world!

Section 44.2: Packages A package is made up of multiple Python files (or modules), and can even include libraries written in C or C++. Instead of being a single file, it is an entire folder structure which might look like this: Folder package __init__.py dog.py hi.py __init__.py from package.dog import woof from package.hi import hi dog.py def woof(): print("WOOF!!!") hi.py def hi(): print("Hello world!")

All Python packages must contain an __init__.py file. When you import a package in your script (import package), the __init__.py script will be run, giving you access to the all of the functions in the package. In this case, it allows you to use the package.hi and package.woof functions.

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Chapter 45: Math Module Section 45.1: Rounding: round, floor, ceil, trunc In addition to the built-in round function, the math module provides the floor, ceil, and trunc functions. x = 1.55 y = -1.55 # round to the nearest integer round(x) # 2 round(y) # -2 # the second argument gives how many decimal places to round to (defaults to 0) round(x, 1) # 1.6 round(y, 1) # -1.6 # math is a module so import it first, then use it. import math # get the largest integer less than x math.floor(x) # 1 math.floor(y) # -2 # get the smallest integer greater than x math.ceil(x) # 2 math.ceil(y) # -1 # drop fractional part of x math.trunc(x) # 1, equivalent to math.floor for positive numbers math.trunc(y) # -1, equivalent to math.ceil for negative numbers

Python 2.x Version

≤ 2.7

floor, ceil, trunc, and round always return a float. round(1.3)

# 1.0

round always breaks ties away from zero. round(0.5) round(1.5)

# 1.0 # 2.0

Python 3.x Version

≥ 3.0

floor, ceil, and trunc always return an Integral value, while round returns an Integral value if called with one

argument. round(1.3) round(1.33, 1)

# 1 # 1.3

round breaks ties towards the nearest even number. This corrects the bias towards larger numbers when

performing a large number of calculations. round(0.5) round(1.5)

# 0 # 2

Warning!

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As with any floating-point representation, some fractions cannot be represented exactly. This can lead to some unexpected rounding behavior. round(2.675, 2)

# 2.67, not 2.68!

Warning about the floor, trunc, and integer division of negative numbers Python (and C++ and Java) round away from zero for negative numbers. Consider: >>> math.floor(-1.7) -2.0 >>> -5 // 2 -3

Section 45.2: Trigonometry Calculating the length of the hypotenuse math.hypot(2, 4) # Just a shorthand for SquareRoot(2**2 + 4**2) # Out: 4.47213595499958

Converting degrees to/from radians All math functions expect radians so you need to convert degrees to radians: math.radians(45) # Out: 0.7853981633974483

# Convert 45 degrees to radians

All results of the inverse trigonometric functions return the result in radians, so you may need to convert it back to degrees: math.degrees(math.asin(1)) # Out: 90.0

# Convert the result of asin to degrees

Sine, cosine, tangent and inverse functions # Sine and arc sine math.sin(math.pi / 2) # Out: 1.0 math.sin(math.radians(90)) # Out: 1.0 math.asin(1) # Out: 1.5707963267948966 math.asin(1) / math.pi # Out: 0.5

# Sine of 90 degrees

# "= pi / 2"

# Cosine and arc cosine: math.cos(math.pi / 2) # Out: 6.123233995736766e-17 # Almost zero but not exactly because "pi" is a float with limited precision! math.acos(1) # Out: 0.0 # Tangent and arc tangent: math.tan(math.pi/2) # Out: 1.633123935319537e+16 # Very large but not exactly "Inf" because "pi" is a float with limited precision

Python 3.x Version

≥ 3.5

math.atan(math.inf)

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# Out: 1.5707963267948966 # This is just "pi / 2" math.atan(float('inf')) # Out: 1.5707963267948966 # This is just "pi / 2"

Apart from the math.atan there is also a two-argument math.atan2 function, which computes the correct quadrant and avoids pitfalls of division by zero: math.atan2(1, 2) # Equivalent to "math.atan(1/2)" # Out: 0.4636476090008061 # ≈ 26.57 degrees, 1st quadrant math.atan2(-1, -2) # Not equal to "math.atan(-1/-2)" == "math.atan(1/2)" # Out: -2.677945044588987 # ≈ -153.43 degrees (or 206.57 degrees), 3rd quadrant math.atan2(1, 0) # math.atan(1/0) would raise ZeroDivisionError # Out: 1.5707963267948966 # This is just "pi / 2"

Hyperbolic sine, cosine and tangent # Hyperbolic sine function math.sinh(math.pi) # = 11.548739357257746 math.asinh(1) # = 0.8813735870195429 # Hyperbolic cosine function math.cosh(math.pi) # = 11.591953275521519 math.acosh(1) # = 0.0 # Hyperbolic tangent function math.tanh(math.pi) # = 0.99627207622075 math.atanh(0.5) # = 0.5493061443340549

Section 45.3: Pow for faster exponentiation Using the timeit module from the command line: > python -m timeit 'for x in xrange(50000): b = x**3' 10 loops, best of 3: 51.2 msec per loop > python -m timeit 'from math import pow' 'for x in xrange(50000): b = pow(x,3)' 100 loops, best of 3: 9.15 msec per loop

The built-in ** operator often comes in handy, but if performance is of the essence, use math.pow. Be sure to note, however, that pow returns floats, even if the arguments are integers: > from math import pow > pow(5,5) 3125.0

Section 45.4: Infinity and NaN ("not a number") In all versions of Python, we can represent infinity and NaN ("not a number") as follows: pos_inf = float('inf') neg_inf = float('-inf') not_a_num = float('nan')

# positive infinity # negative infinity # NaN ("not a number")

In Python 3.5 and higher, we can also use the defined constants math.inf and math.nan: Python 3.x Version

≥ 3.5

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pos_inf = math.inf neg_inf = -math.inf not_a_num = math.nan

The string representations display as inf and -inf and nan: pos_inf, neg_inf, not_a_num # Out: (inf, -inf, nan)

We can test for either positive or negative infinity with the isinf method: math.isinf(pos_inf) # Out: True math.isinf(neg_inf) # Out: True

We can test specifically for positive infinity or for negative infinity by direct comparison: pos_inf == float('inf') # Out: True

# or

== math.inf in Python 3.5+

neg_inf == float('-inf') # Out: True

# or

== -math.inf in Python 3.5+

neg_inf == pos_inf # Out: False

Python 3.2 and higher also allows checking for finiteness: Python 3.x Version

≥ 3.2

math.isfinite(pos_inf) # Out: False math.isfinite(0.0) # Out: True

Comparison operators work as expected for positive and negative infinity: import sys sys.float_info.max # Out: 1.7976931348623157e+308

(this is system-dependent)

pos_inf > sys.float_info.max # Out: True neg_inf < -sys.float_info.max # Out: True

But if an arithmetic expression produces a value larger than the maximum that can be represented as a float, it will become infinity: pos_inf == sys.float_info.max * 1.0000001 # Out: True neg_inf == -sys.float_info.max * 1.0000001

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# Out: True

However division by zero does not give a result of infinity (or negative infinity where appropriate), rather it raises a ZeroDivisionError exception. try: x = 1.0 / 0.0 print(x) except ZeroDivisionError: print("Division by zero") # Out: Division by zero

Arithmetic operations on infinity just give infinite results, or sometimes NaN: -5.0 * pos_inf == neg_inf # Out: True -5.0 * neg_inf == pos_inf # Out: True pos_inf * neg_inf == neg_inf # Out: True 0.0 * pos_inf # Out: nan 0.0 * neg_inf # Out: nan pos_inf / pos_inf # Out: nan

NaN is never equal to anything, not even itself. We can test for it is with the isnan method: not_a_num == not_a_num # Out: False math.isnan(not_a_num) Out: True

NaN always compares as "not equal", but never less than or greater than: not_a_num != 5.0 # Out: True not_a_num > 5.0 # Out: False

# or any random value

or

not_a_num < 5.0

or

not_a_num == 5.0

Arithmetic operations on NaN always give NaN. This includes multiplication by -1: there is no "negative NaN". 5.0 * not_a_num # Out: nan float('-nan') # Out: nan

Python 3.x Version

≥ 3.5

-math.nan

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# Out: nan

There is one subtle difference between the old float versions of NaN and infinity and the Python 3.5+ math library constants: Python 3.x Version

≥ 3.5

math.inf is math.inf, math.nan is math.nan # Out: (True, True) float('inf') is float('inf'), float('nan') is float('nan') # Out: (False, False)

Section 45.5: Logarithms math.log(x) gives the natural (base e) logarithm of x. math.log(math.e) math.log(1) math.log(100)

# 1.0 # 0.0 # 4.605170185988092

math.log can lose precision with numbers close to 1, due to the limitations of floating-point numbers. In order to

accurately calculate logs close to 1, use math.log1p, which evaluates the natural logarithm of 1 plus the argument: math.log(1 + 1e-20) math.log1p(1e-20)

# 0.0 # 1e-20

math.log10 can be used for logs base 10: math.log10(10)

Python 2.x Version

# 1.0 ≥ 2.3.0

When used with two arguments, math.log(x, base) gives the logarithm of x in the given base (i.e. log(x) / log(base). math.log(100, 10) # 2.0 math.log(27, 3) # 3.0 math.log(1, 10) # 0.0

Section 45.6: Constants math modules includes two commonly used mathematical constants. math.pi - The mathematical constant pi math.e - The mathematical constant e (base of natural logarithm) >>> from math import pi, e >>> pi 3.141592653589793 >>> e 2.718281828459045 >>>

Python 3.5 and higher have constants for infinity and NaN ("not a number"). The older syntax of passing a string to float() still works.

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Python 3.x Version

≥ 3.5

math.inf == float('inf') # Out: True -math.inf == float('-inf') # Out: True # NaN never compares equal to anything, even itself math.nan == float('nan') # Out: False

Section 45.7: Imaginary Numbers Imaginary numbers in Python are represented by a "j" or "J" trailing the target number. 1j 1j * 1j

# Equivalent to the square root of -1. # = (-1+0j)

Section 45.8: Copying signs In Python 2.6 and higher, math.copysign(x, y) returns x with the sign of y. The returned value is always a float. Python 2.x Version

≥ 2.6

math.copysign(-2, 3) math.copysign(3, -3) math.copysign(4, 14.2) math.copysign(1, -0.0)

# # # #

2.0 -3.0 4.0 -1.0, on a platform which supports signed zero

Section 45.9: Complex numbers and the cmath module The cmath module is similar to the math module, but defines functions appropriately for the complex plane. First of all, complex numbers are a numeric type that is part of the Python language itself rather than being provided by a library class. Thus we don't need to import cmath for ordinary arithmetic expressions. Note that we use j (or J) and not i. z = 1 + 3j

We must use 1j since j would be the name of a variable rather than a numeric literal. 1j * 1j Out: (-1+0j) 1j ** 1j # Out: (0.20787957635076193+0j)

# "i to the i"

==

math.e ** -(math.pi/2)

We have the real part and the imag (imaginary) part, as well as the complex conjugate: # real part and imaginary part are both float type z.real, z.imag # Out: (1.0, 3.0) z.conjugate() # Out: (1-3j)

# z.conjugate() == z.real - z.imag * 1j

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The built-in functions abs and complex are also part of the language itself and don't require any import: abs(1 + 1j) # Out: 1.4142135623730951

# square root of 2

complex(1) # Out: (1+0j) complex(imag=1) # Out: (1j) complex(1, 1) # Out: (1+1j)

The complex function can take a string, but it can't have spaces: complex('1+1j') # Out: (1+1j) complex('1 + 1j') # Exception: ValueError: complex() arg is a malformed string

But for most functions we do need the module, for instance sqrt: import cmath cmath.sqrt(-1) # Out: 1j

Naturally the behavior of sqrt is different for complex numbers and real numbers. In non-complex math the square root of a negative number raises an exception: import math math.sqrt(-1) # Exception: ValueError: math domain error

Functions are provided to convert to and from polar coordinates: cmath.polar(1 + 1j) # Out: (1.4142135623730951, 0.7853981633974483)

# == (sqrt(1 + 1), atan2(1, 1))

abs(1 + 1j), cmath.phase(1 + 1j) # Out: (1.4142135623730951, 0.7853981633974483)

# same as previous calculation

cmath.rect(math.sqrt(2), math.atan(1)) # Out: (1.0000000000000002+1.0000000000000002j)

The mathematical field of complex analysis is beyond the scope of this example, but many functions in the complex plane have a "branch cut", usually along the real axis or the imaginary axis. Most modern platforms support "signed zero" as specified in IEEE 754, which provides continuity of those functions on both sides of the branch cut. The following example is from the Python documentation: cmath.phase(complex(-1.0, 0.0)) # Out: 3.141592653589793 cmath.phase(complex(-1.0, -0.0))

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# Out: -3.141592653589793

The cmath module also provides many functions with direct counterparts from the math module. In addition to sqrt, there are complex versions of exp, log, log10, the trigonometric functions and their inverses (sin, cos, tan, asin, acos, atan), and the hyperbolic functions and their inverses (sinh, cosh, tanh, asinh, acosh, atanh). Note however there is no complex counterpart of math.atan2, the two-argument form of arctangent. cmath.log(1+1j) # Out: (0.34657359027997264+0.7853981633974483j) cmath.exp(1j * cmath.pi) # Out: (-1+1.2246467991473532e-16j)

# e to the i pi == -1, within rounding error

The constants pi and e are provided. Note these are float and not complex. type(cmath.pi) # Out:

The cmath module also provides complex versions of isinf, and (for Python 3.2+) isfinite. See "Infinity and NaN". A complex number is considered infinite if either its real part or its imaginary part is infinite. cmath.isinf(complex(float('inf'), 0.0)) # Out: True

Likewise, the cmath module provides a complex version of isnan. See "Infinity and NaN". A complex number is considered "not a number" if either its real part or its imaginary part is "not a number". cmath.isnan(0.0, float('nan')) # Out: True

Note there is no cmath counterpart of the math.inf and math.nan constants (from Python 3.5 and higher) Python 3.x Version

≥ 3.5

cmath.isinf(complex(0.0, math.inf)) # Out: True cmath.isnan(complex(math.nan, 0.0)) # Out: True cmath.inf # Exception: AttributeError: module 'cmath' has no attribute 'inf'

In Python 3.5 and higher, there is an isclose method in both cmath and math modules. Python 3.x Version

≥ 3.5

z = cmath.rect(*cmath.polar(1+1j)) z # Out: (1.0000000000000002+1.0000000000000002j) cmath.isclose(z, 1+1j) # True

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Chapter 46: Complex math Section 46.1: Advanced complex arithmetic The module cmath includes additional functions to use complex numbers. import cmath

This module can calculate the phase of a complex number, in radians: z = 2+3j # A complex number cmath.phase(z) # 0.982793723247329

It allows the conversion between the cartesian (rectangular) and polar representations of complex numbers: cmath.polar(z) # (3.605551275463989, 0.982793723247329) cmath.rect(2, cmath.pi/2) # (0+2j)

The module contains the complex version of Exponential and logarithmic functions (as usual, log is the natural logarithm and log10 the decimal logarithm): cmath.exp(z) # (-7.315110094901103+1.0427436562359045j) cmath.log(z) # (1.2824746787307684+0.982793723247329j) cmath.log10(-100) # (2+1.3643763538418412j)

Square roots: cmath.sqrt(z) # (1.6741492280355401+0.8959774761298381j)

Trigonometric functions and their inverses: cmath.sin(z) # cmath.cos(z) # cmath.tan(z) # cmath.asin(z) # cmath.acos(z) # cmath.atan(z) # cmath.sin(z)**2

(9.15449914691143-4.168906959966565j) (-4.189625690968807-9.109227893755337j) (-0.003764025641504249+1.00323862735361j) (0.5706527843210994+1.9833870299165355j) (1.0001435424737972-1.9833870299165355j) (1.4099210495965755+0.22907268296853878j) + cmath.cos(z)**2 # (1+0j)

Hyperbolic functions and their inverses: cmath.sinh(z) # (-3.59056458998578+0.5309210862485197j) cmath.cosh(z) # (-3.7245455049153224+0.5118225699873846j) cmath.tanh(z) # (0.965385879022133-0.009884375038322495j) cmath.asinh(z) # (0.5706527843210994+1.9833870299165355j) cmath.acosh(z) # (1.9833870299165355+1.0001435424737972j) cmath.atanh(z) # (0.14694666622552977+1.3389725222944935j) cmath.cosh(z)**2 - cmath.sin(z)**2 # (1+0j) cmath.cosh((0+1j)*z) - cmath.cos(z) # 0j

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Section 46.2: Basic complex arithmetic Python has built-in support for complex arithmetic. The imaginary unit is denoted by j: z = 2+3j # A complex number w = 1-7j # Another complex number

Complex numbers can be summed, subtracted, multiplied, divided and exponentiated: z + w z - w z * w z / w z**3

# # # # #

(3-4j) (1+10j) (23-11j) (-0.38+0.34j) (-46+9j)

Python can also extract the real and imaginary parts of complex numbers, and calculate their absolute value and conjugate: z.real # 2.0 z.imag # 3.0 abs(z) # 3.605551275463989 z.conjugate() # (2-3j)

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Chapter 47: Collections module The built-in collections package provides several specialized, flexible collection types that are both highperformance and provide alternatives to the general collection types of dict, list, tuple and set. The module also defines abstract base classes describing different types of collection functionality (such as MutableSet and ItemsView).

Section 47.1: collections.Counter Counter is a dict sub class that allows you to easily count objects. It has utility methods for working with the frequencies of the objects that you are counting. import collections counts = collections.Counter([1,2,3])

the above code creates an object, counts, which has the frequencies of all the elements passed to the constructor. This example has the value Counter({1: 1, 2: 1, 3: 1}) Constructor examples Letter Counter >>> collections.Counter('Happy Birthday') Counter({'a': 2, 'p': 2, 'y': 2, 'i': 1, 'r': 1, 'B': 1, ' ': 1, 'H': 1, 'd': 1, 'h': 1, 't': 1})

Word Counter >>> collections.Counter('I am Sam Sam I am That Sam-I-am That Sam-I-am! I do not like that Sam-Iam'.split()) Counter({'I': 3, 'Sam': 2, 'Sam-I-am': 2, 'That': 2, 'am': 2, 'do': 1, 'Sam-I-am!': 1, 'that': 1, 'not': 1, 'like': 1})

Recipes >>> c = collections.Counter({'a': 4, 'b': 2, 'c': -2, 'd': 0})

Get count of individual element >>> c['a'] 4

Set count of individual element >>> c['c'] = -3 >>> c Counter({'a': 4, 'b': 2, 'd': 0, 'c': -3})

Get total number of elements in counter (4 + 2 + 0 - 3) >>> sum(c.itervalues()) 3

# negative numbers are counted!

Get elements (only those with positive counter are kept)

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>>> list(c.elements()) ['a', 'a', 'a', 'a', 'b', 'b']

Remove keys with 0 or negative value >>> c - collections.Counter() Counter({'a': 4, 'b': 2})

Remove everything >>> c.clear() >>> c Counter()

Add remove individual elements >>> c.update({'a': 3, 'b':3}) >>> c.update({'a': 2, 'c':2}) # adds to existing, sets if they don't exist >>> c Counter({'a': 5, 'b': 3, 'c': 2}) >>> c.subtract({'a': 3, 'b': 3, 'c': 3}) # subtracts (negative values are allowed) >>> c Counter({'a': 2, 'b': 0, 'c': -1})

Section 47.2: collections.OrderedDict The order of keys in Python dictionaries is arbitrary: they are not governed by the order in which you add them. For example: >>> d = {'foo': 5, 'bar': 6} >>> print(d) {'foo': 5, 'bar': 6} >>> d['baz'] = 7 >>> print(a) {'baz': 7, 'foo': 5, 'bar': 6} >>> d['foobar'] = 8 >>> print(a) {'baz': 7, 'foo': 5, 'bar': 6, 'foobar': 8} ```

(The arbitrary ordering implied above means that you may get different results with the above code to that shown here.) The order in which the keys appear is the order which they would be iterated over, e.g. using a for loop. The collections.OrderedDict class provides dictionary objects that retain the order of keys. OrderedDicts can be created as shown below with a series of ordered items (here, a list of tuple key-value pairs): >>> from collections import OrderedDict >>> d = OrderedDict([('foo', 5), ('bar', 6)]) >>> print(d) OrderedDict([('foo', 5), ('bar', 6)]) >>> d['baz'] = 7 >>> print(d) OrderedDict([('foo', 5), ('bar', 6), ('baz', 7)]) >>> d['foobar'] = 8

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>>> print(d) OrderedDict([('foo', 5), ('bar', 6), ('baz', 7), ('foobar', 8)])

Or we can create an empty OrderedDict and then add items: >>> o = OrderedDict() >>> o['key1'] = "value1" >>> o['key2'] = "value2" >>> print(o) OrderedDict([('key1', 'value1'), ('key2', 'value2')])

Iterating through an OrderedDict allows key access in the order they were added. What happens if we assign a new value to an existing key? >>> d['foo'] = 4 >>> print(d) OrderedDict([('foo', 4), ('bar', 6), ('baz', 7), ('foobar', 8)])

The key retains its original place in the OrderedDict.

Section 47.3: collections.defaultdict collections.defaultdict(default_factory) returns a subclass of dict that has a default value for missing keys. The argument should be a function that returns the default value when called with no arguments. If there is nothing passed, it defaults to None. >>> state_capitals = collections.defaultdict(str) >>> state_capitals defaultdict(, {})

returns a reference to a defaultdict that will create a string object with its default_factory method. A typical usage of defaultdict is to use one of the builtin types such as str, int, list or dict as the default_factory, since these return empty types when called with no arguments: >>> str() '' >>> int() 0 >>> list []

Calling the defaultdict with a key that does not exist does not produce an error as it would in a normal dictionary. >>> state_capitals['Alaska'] '' >>> state_capitals defaultdict(, {'Alaska': ''})

Another example with int: >>> fruit_counts = defaultdict(int) >>> fruit_counts['apple'] += 2 # No errors should occur >>> fruit_counts default_dict(int, {'apple': 2}) >>> fruit_counts['banana'] # No errors should occur

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0 >>> fruit_counts # A new key is created default_dict(int, {'apple': 2, 'banana': 0})

Normal dictionary methods work with the default dictionary >>> state_capitals['Alabama'] = 'Montgomery' >>> state_capitals defaultdict(, {'Alabama': 'Montgomery', 'Alaska': ''})

Using list as the default_factory will create a list for each new key. >>> s = [('NC', 'Raleigh'), ('VA', 'Richmond'), ('WA', 'Seattle'), ('NC', 'Asheville')] >>> dd = collections.defaultdict(list) >>> for k, v in s: ... dd[k].append(v) >>> dd defaultdict(, {'VA': ['Richmond'], 'NC': ['Raleigh', 'Asheville'], 'WA': ['Seattle']})

Section 47.4: collections.namedtuple Define a new type Person using namedtuple like this: Person = namedtuple('Person', ['age', 'height', 'name'])

The second argument is the list of attributes that the tuple will have. You can list these attributes also as either space or comma separated string: Person = namedtuple('Person', 'age, height, name')

or Person = namedtuple('Person', 'age height name')

Once defined, a named tuple can be instantiated by calling the object with the necessary parameters, e.g.: dave = Person(30, 178, 'Dave')

Named arguments can also be used: jack = Person(age=30, height=178, name='Jack S.')

Now you can access the attributes of the namedtuple: print(jack.age) # 30 print(jack.name) # 'Jack S.'

The first argument to the namedtuple constructor (in our example 'Person') is the typename. It is typical to use the same word for the constructor and the typename, but they can be different: Human = namedtuple('Person', 'age, height, name') dave = Human(30, 178, 'Dave')

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print(dave)

# yields: Person(age=30, height=178, name='Dave')

Section 47.5: collections.deque Returns a new deque object initialized left-to-right (using append()) with data from iterable. If iterable is not specified, the new deque is empty. Deques are a generalization of stacks and queues (the name is pronounced “deck” and is short for “double-ended queue”). Deques support thread-safe, memory efficient appends and pops from either side of the deque with approximately the same O(1) performance in either direction. Though list objects support similar operations, they are optimized for fast fixed-length operations and incur O(n) memory movement costs for pop(0) and insert(0, v) operations which change both the size and position of the underlying data representation. New in version 2.4. If maxlen is not specified or is None, deques may grow to an arbitrary length. Otherwise, the deque is bounded to the specified maximum length. Once a bounded length deque is full, when new items are added, a corresponding number of items are discarded from the opposite end. Bounded length deques provide functionality similar to the tail filter in Unix. They are also useful for tracking transactions and other pools of data where only the most recent activity is of interest. Changed in version 2.6: Added maxlen parameter. >>> from collections import deque >>> d = deque('ghi') >>> for elem in d: ... print elem.upper() G H I

# make a new deque with three items # iterate over the deque's elements

>>> d.append('j') >>> d.appendleft('f') >>> d deque(['f', 'g', 'h', 'i', 'j'])

# add a new entry to the right side # add a new entry to the left side # show the representation of the deque

>>> d.pop() 'j' >>> d.popleft() 'f' >>> list(d) ['g', 'h', 'i'] >>> d[0] 'g' >>> d[-1] 'i'

# return and remove the rightmost item # return and remove the leftmost item # list the contents of the deque # peek at leftmost item # peek at rightmost item

>>> list(reversed(d)) # ['i', 'h', 'g'] >>> 'h' in d # True >>> d.extend('jkl') # >>> d deque(['g', 'h', 'i', 'j', 'k', 'l']) >>> d.rotate(1) # >>> d

list the contents of a deque in reverse search the deque add multiple elements at once

right rotation

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deque(['l', 'g', 'h', 'i', 'j', 'k']) >>> d.rotate(-1) # left rotation >>> d deque(['g', 'h', 'i', 'j', 'k', 'l']) >>> deque(reversed(d)) # make a new deque in reverse order deque(['l', 'k', 'j', 'i', 'h', 'g']) >>> d.clear() # empty the deque >>> d.pop() # cannot pop from an empty deque Traceback (most recent call last): File "", line 1, in -topleveld.pop() IndexError: pop from an empty deque >>> d.extendleft('abc') >>> d deque(['c', 'b', 'a'])

# extendleft() reverses the input order

Source: https://docs.python.org/2/library/collections.html

Section 47.6: collections.ChainMap ChainMap is new in version 3.3

Returns a new ChainMap object given a number of maps. This object groups multiple dicts or other mappings together to create a single, updateable view. ChainMaps are useful managing nested contexts and overlays. An example in the python world is found in the

implementation of the Context class in Django's template engine. It is useful for quickly linking a number of mappings so that the result can be treated as a single unit. It is often much faster than creating a new dictionary and running multiple update() calls. Anytime one has a chain of lookup values there can be a case for ChainMap. An example includes having both user specified values and a dictionary of default values. Another example is the POST and GET parameter maps found in web use, e.g. Django or Flask. Through the use of ChainMap one returns a combined view of two distinct dictionaries. The maps parameter list is ordered from first-searched to last-searched. Lookups search the underlying mappings successively until a key is found. In contrast, writes, updates, and deletions only operate on the first mapping. import collections # define two dictionaries with at least some keys overlapping. dict1 = {'apple': 1, 'banana': 2} dict2 = {'coconut': 1, 'date': 1, 'apple': 3} # create two ChainMaps with different ordering of those dicts. combined_dict = collections.ChainMap(dict1, dict2) reverse_ordered_dict = collections.ChainMap(dict2, dict1)

Note the impact of order on which value is found first in the subsequent lookup for k, v in combined_dict.items(): print(k, v) date 1 apple 1 banana 2

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coconut 1 for k, v in reverse_ordered_dict.items(): print(k, v) date 1 apple 3 banana 2 coconut 1

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Chapter 48: Operator module Section 48.1: Itemgetter Grouping the key-value pairs of a dictionary by the value with itemgetter: from itertools import groupby from operator import itemgetter adict = {'a': 1, 'b': 5, 'c': 1} dict((i, dict(v)) for i, v in groupby(adict.items(), itemgetter(1))) # Output: {1: {'a': 1, 'c': 1}, 5: {'b': 5}}

which is equivalent (but faster) to a lambda function like this: dict((i, dict(v)) for i, v in groupby(adict.items(), lambda x: x[1]))

Or sorting a list of tuples by the second element first the first element as secondary: alist_of_tuples = [(5,2), (1,3), (2,2)] sorted(alist_of_tuples, key=itemgetter(1,0)) # Output: [(2, 2), (5, 2), (1, 3)]

Section 48.2: Operators as alternative to an infix operator For every infix operator, e.g. + there is an operator-function (operator.add for +): 1 + 1 # Output: 2 from operator import add add(1, 1) # Output: 2

even though the main documentation states that for the arithmetic operators only numerical input is allowed it is possible: from operator import mul mul('a', 10) # Output: 'aaaaaaaaaa' mul([3], 3) # Output: [3, 3, 3]

See also: mapping from operation to operator function in the official Python documentation.

Section 48.3: Methodcaller Instead of this lambda-function that calls the method explicitly: alist = ['wolf', 'sheep', 'duck'] list(filter(lambda x: x.startswith('d'), alist)) # Output: ['duck']

# Keep only elements that start with 'd'

one could use a operator-function that does the same: from operator import methodcaller

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list(filter(methodcaller('startswith', 'd'), alist)) # Does the same but is faster. # Output: ['duck']

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Chapter 49: JSON Module Section 49.1: Storing data in a file The following snippet encodes the data stored in d into JSON and stores it in a file (replace filename with the actual name of the file). import json d = { 'foo': 'bar', 'alice': 1, 'wonderland': [1, 2, 3] } with open(filename, 'w') as f: json.dump(d, f)

Section 49.2: Retrieving data from a file The following snippet opens a JSON encoded file (replace filename with the actual name of the file) and returns the object that is stored in the file. import json with open(filename, 'r') as f: d = json.load(f)

Section 49.3: Formatting JSON output Let's say we have the following data: >>> data = {"cats": [{"name": "Tubbs", "color": "white"}, {"name": "Pepper", "color": "black"}]}

Just dumping this as JSON does not do anything special here: >>> print(json.dumps(data)) {"cats": [{"name": "Tubbs", "color": "white"}, {"name": "Pepper", "color": "black"}]}

Setting indentation to get prettier output If we want pretty printing, we can set an indent size: >>> print(json.dumps(data, indent=2)) { "cats": [ { "name": "Tubbs", "color": "white" }, { "name": "Pepper", "color": "black" } ] }

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Sorting keys alphabetically to get consistent output By default the order of keys in the output is undefined. We can get them in alphabetical order to make sure we always get the same output: >>> print(json.dumps(data, sort_keys=True)) {"cats": [{"color": "white", "name": "Tubbs"}, {"color": "black", "name": "Pepper"}]}

Getting rid of whitespace to get compact output We might want to get rid of the unnecessary spaces, which is done by setting separator strings different from the default ', ' and ': ': >>>print(json.dumps(data, separators=(',', ':'))) {"cats":[{"name":"Tubbs","color":"white"},{"name":"Pepper","color":"black"}]}

Section 49.4: `load` vs `loads`, `dump` vs `dumps` The json module contains functions for both reading and writing to and from unicode strings, and reading and writing to and from files. These are differentiated by a trailing s in the function name. In these examples we use a StringIO object, but the same functions would apply for any file-like object. Here we use the string-based functions: import json data = {u"foo": u"bar", u"baz": []} json_string = json.dumps(data) # u'{"foo": "bar", "baz": []}' json.loads(json_string) # {u"foo": u"bar", u"baz": []}

And here we use the file-based functions: import json from io import StringIO json_file = StringIO() data = {u"foo": u"bar", u"baz": []} json.dump(data, json_file) json_file.seek(0) # Seek back to the start of the file before reading json_file_content = json_file.read() # u'{"foo": "bar", "baz": []}' json_file.seek(0) # Seek back to the start of the file before reading json.load(json_file) # {u"foo": u"bar", u"baz": []}

As you can see the main difference is that when dumping json data you must pass the file handle to the function, as opposed to capturing the return value. Also worth noting is that you must seek to the start of the file before reading or writing, in order to avoid data corruption. When opening a file the cursor is placed at position 0, so the below would also work: import json json_file_path = './data.json' data = {u"foo": u"bar", u"baz": []}

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with open(json_file_path, 'w') as json_file: json.dump(data, json_file) with open(json_file_path) as json_file: json_file_content = json_file.read() # u'{"foo": "bar", "baz": []}' with open(json_file_path) as json_file: json.load(json_file) # {u"foo": u"bar", u"baz": []}

Having both ways of dealing with json data allows you to idiomatically and efficiently work with formats which build upon json, such as pyspark's json-per-line: # loading from a file data = [json.loads(line) for line in open(file_path).splitlines()] # dumping to a file with open(file_path, 'w') as json_file: for item in data: json.dump(item, json_file) json_file.write('\n')

Section 49.5: Calling `json.tool` from the command line to pretty-print JSON output Given some JSON file "foo.json" like: {"foo": {"bar": {"baz": 1}}}

we can call the module directly from the command line (passing the filename as an argument) to pretty-print it: $ python -m json.tool foo.json { "foo": { "bar": { "baz": 1 } } }

The module will also take input from STDOUT, so (in Bash) we equally could do: $ cat foo.json | python -m json.tool

Section 49.6: JSON encoding custom objects If we just try the following: import json from datetime import datetime data = {'datetime': datetime(2016, 9, 26, 4, 44, 0)} print(json.dumps(data))

we get an error saying TypeError: datetime.datetime(2016, 9, 26, 4, 44) is not JSON serializable. To be able to serialize the datetime object properly, we need to write custom code for how to convert it: GoalKicker.com – Python® Notes for Professionals

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class DatetimeJSONEncoder(json.JSONEncoder): def default(self, obj): try: return obj.isoformat() except AttributeError: # obj has no isoformat method; let the builtin JSON encoder handle it return super(DatetimeJSONEncoder, self).default(obj)

and then use this encoder class instead of json.dumps: encoder = DatetimeJSONEncoder() print(encoder.encode(data)) # prints {"datetime": "2016-09-26T04:44:00"}

Section 49.7: Creating JSON from Python dict import json d = { 'foo': 'bar', 'alice': 1, 'wonderland': [1, 2, 3] } json.dumps(d)

The above snippet will return the following: '{"wonderland": [1, 2, 3], "foo": "bar", "alice": 1}'

Section 49.8: Creating Python dict from JSON import json s = '{"wonderland": [1, 2, 3], "foo": "bar", "alice": 1}' json.loads(s)

The above snippet will return the following: {u'alice': 1, u'foo': u'bar', u'wonderland': [1, 2, 3]}

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Chapter 50: Sqlite3 Module Section 50.1: Sqlite3 - Not require separate server process The sqlite3 module was written by Gerhard Häring. To use the module, you must first create a Connection object that represents the database. Here the data will be stored in the example.db file: import sqlite3 conn = sqlite3.connect('example.db')

You can also supply the special name :memory: to create a database in RAM. Once you have a Connection, you can create a Cursor object and call its execute() method to perform SQL commands: c = conn.cursor() # Create table c.execute('''CREATE TABLE stocks (date text, trans text, symbol text, qty real, price real)''') # Insert a row of data c.execute("INSERT INTO stocks VALUES ('2006-01-05','BUY','RHAT',100,35.14)") # Save (commit) the changes conn.commit() # We can also close the connection if we are done with it. # Just be sure any changes have been committed or they will be lost. conn.close()

Section 50.2: Getting the values from the database and Error handling Fetching the values from the SQLite3 database. Print row values returned by select query import sqlite3 conn = sqlite3.connect('example.db') c = conn.cursor() c.execute("SELECT * from table_name where id=cust_id") for row in c: print row # will be a list

To fetch single matching fetchone() method print c.fetchone()

For multiple rows use fetchall() method a=c.fetchall() #which is similar to list(cursor) method used previously for row in a: print row

Error handling can be done using sqlite3.Error built in function

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try: #SQL Code except sqlite3.Error as e: print "An error occurred:", e.args[0]

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Chapter 51: The os Module Parameter Details A path to a file. The path separator may be determined by os.path.sep. Path The desired permission, in octal (e.g. 0700)

Mode

This module provides a portable way of using operating system dependent functionality.

Section 51.1: makedirs - recursive directory creation Given a local directory with the following contents: └── dir1 ├── subdir1 └── subdir2

We want to create the same subdir1, subdir2 under a new directory dir2, which does not exist yet. import os os.makedirs("./dir2/subdir1") os.makedirs("./dir2/subdir2")

Running this results in ├── dir1 │

├── subdir1

│

└── subdir2

└── dir2 ├── subdir1 └── subdir2

dir2 is only created the first time it is needed, for subdir1's creation. If we had used os.mkdir instead, we would have had an exception because dir2 would not have existed yet. os.mkdir("./dir2/subdir1") OSError: [Errno 2] No such file or directory: './dir2/subdir1'

os.makedirs won't like it if the target directory exists already. If we re-run it again: OSError: [Errno 17] File exists: './dir2/subdir1'

However, this could easily be fixed by catching the exception and checking that the directory has been created. try: os.makedirs("./dir2/subdir1") except OSError: if not os.path.isdir("./dir2/subdir1"): raise try: os.makedirs("./dir2/subdir2") except OSError:

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if not os.path.isdir("./dir2/subdir2"): raise

Section 51.2: Create a directory os.mkdir('newdir')

If you need to specify permissions, you can use the optional mode argument: os.mkdir('newdir', mode=0700)

Section 51.3: Get current directory Use the os.getcwd() function: print(os.getcwd())

Section 51.4: Determine the name of the operating system The os module provides an interface to determine what type of operating system the code is currently running on. os.name

This can return one of the following in Python 3: posix nt ce java

More detailed information can be retrieved from sys.platform

Section 51.5: Remove a directory Remove the directory at path: os.rmdir(path)

You should not use os.remove() to remove a directory. That function is for files and using it on directories will result in an OSError

Section 51.6: Follow a symlink (POSIX) Sometimes you need to determine the target of a symlink. os.readlink will do this: print(os.readlink(path_to_symlink))

Section 51.7: Change permissions on a file os.chmod(path, mode)

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Chapter 52: The locale Module Section 52.1: Currency Formatting US Dollars Using the locale Module import locale locale.setlocale(locale.LC_ALL, '') Out[2]: 'English_United States.1252' locale.currency(762559748.49) Out[3]: '$762559748.49' locale.currency(762559748.49, grouping=True) Out[4]: '$762,559,748.49'

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Chapter 53: Itertools Module Section 53.1: Combinations method in Itertools Module itertools.combinations will return a generator of the k-combination sequence of a list.

In other words: It will return a generator of tuples of all the possible k-wise combinations of the input list. For Example: If you have a list: a = [1,2,3,4,5] b = list(itertools.combinations(a, 2)) print b

Output: [(1, 2), (1, 3), (1, 4), (1, 5), (2, 3), (2, 4), (2, 5), (3, 4), (3, 5), (4, 5)]

The above output is a generator converted to a list of tuples of all the possible pair-wise combinations of the input list a You can also find all the 3-combinations: a = [1,2,3,4,5] b = list(itertools.combinations(a, 3)) print b

Output: [(1, 2, 3), (1, 2, 4), (1, 2, 5), (1, 3, 4), (1, 3, 5), (1, 4, 5), (2, 3, 4), (2, 3, 5), (2, 4, 5), (3, 4, 5)]

Section 53.2: itertools.dropwhile itertools.dropwhile enables you to take items from a sequence after a condition first becomes False. def is_even(x): return x % 2 == 0

lst = [0, 2, 4, 12, 18, 13, 14, 22, 23, 44] result = list(itertools.dropwhile(is_even, lst)) print(result)

This outputs [13, 14, 22, 23, 44]. (This example is same as the example for takewhile but using dropwhile.) Note that, the first number that violates the predicate (i.e.: the function returning a Boolean value) is_even is, 13. All the elements before that, are discarded. GoalKicker.com – Python® Notes for Professionals

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The output produced by dropwhile is similar to the output generated from the code below. def dropwhile(predicate, iterable): iterable = iter(iterable) for x in iterable: if not predicate(x): yield x break for x in iterable: yield x

The concatenation of results produced by takewhile and dropwhile produces the original iterable. result = list(itertools.takewhile(is_even, lst)) + list(itertools.dropwhile(is_even, lst))

Section 53.3: Zipping two iterators until they are both exhausted Similar to the built-in function zip(), itertools.zip_longest will continue iterating beyond the end of the shorter of two iterables. from itertools import zip_longest a = [i for i in range(5)] # Length is 5 b = ['a', 'b', 'c', 'd', 'e', 'f', 'g'] # Length is 7 for i in zip_longest(a, b): x, y = i # Note that zip longest returns the values as a tuple print(x, y)

An optional fillvalue argument can be passed (defaults to '') like so: for i in zip_longest(a, b, fillvalue='Hogwash!'): x, y = i # Note that zip longest returns the values as a tuple print(x, y)

In Python 2.6 and 2.7, this function is called itertools.izip_longest.

Section 53.4: Take a slice of a generator Itertools "islice" allows you to slice a generator: results = fetch_paged_results() # returns a generator limit = 20 # Only want the first 20 results for data in itertools.islice(results, limit): print(data)

Normally you cannot slice a generator: def gen(): n = 0 while n < 20: n += 1 yield n for part in gen()[:3]: print(part)

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Traceback (most recent call last): File "gen.py", line 6, in for part in gen()[:3]: TypeError: 'generator' object is not subscriptable

However, this works: import itertools def gen(): n = 0 while n < 20: n += 1 yield n for part in itertools.islice(gen(), 3): print(part)

Note that like a regular slice, you can also use start, stop and step arguments: itertools.islice(iterable, 1, 30, 3)

Section 53.5: Grouping items from an iterable object using a function Start with an iterable which needs to be grouped lst = [("a", 5, 6), ("b", 2, 4), ("a", 2, 5), ("c", 2, 6)]

Generate the grouped generator, grouping by the second element in each tuple: def testGroupBy(lst): groups = itertools.groupby(lst, key=lambda x: x[1]) for key, group in groups: print(key, list(group)) testGroupBy(lst) # 5 [('a', 5, 6)] # 2 [('b', 2, 4), ('a', 2, 5), ('c', 2, 6)]

Only groups of consecutive elements are grouped. You may need to sort by the same key before calling groupby For E.g, (Last element is changed) lst = [("a", 5, 6), ("b", 2, 4), ("a", 2, 5), ("c", 5, 6)] testGroupBy(lst) # 5 [('a', 5, 6)] # 2 [('b', 2, 4), ('a', 2, 5)] # 5 [('c', 5, 6)]

The group returned by groupby is an iterator that will be invalid before next iteration. E.g the following will not work if you want the groups to be sorted by key. Group 5 is empty below because when group 2 is fetched it invalidates 5 lst = [("a", 5, 6), ("b", 2, 4), ("a", 2, 5), ("c", 2, 6)] groups = itertools.groupby(lst, key=lambda x: x[1]) for key, group in sorted(groups):

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print(key, list(group)) # 2 [('c', 2, 6)] # 5 []

To correctly do sorting, create a list from the iterator before sorting groups = itertools.groupby(lst, key=lambda x: x[1]) for key, group in sorted((key, list(group)) for key, group in groups): print(key, list(group)) # 2 [('b', 2, 4), ('a', 2, 5), ('c', 2, 6)] # 5 [('a', 5, 6)]

Section 53.6: itertools.takewhile itertools.takewhile enables you to take items from a sequence until a condition first becomes False. def is_even(x): return x % 2 == 0

lst = [0, 2, 4, 12, 18, 13, 14, 22, 23, 44] result = list(itertools.takewhile(is_even, lst)) print(result)

This outputs [0, 2, 4, 12, 18]. Note that, the first number that violates the predicate (i.e.: the function returning a Boolean value) is_even is, 13. Once takewhile encounters a value that produces False for the given predicate, it breaks out. The output produced by takewhile is similar to the output generated from the code below. def takewhile(predicate, iterable): for x in iterable: if predicate(x): yield x else: break

Note: The concatenation of results produced by takewhile and dropwhile produces the original iterable. result = list(itertools.takewhile(is_even, lst)) + list(itertools.dropwhile(is_even, lst))

Section 53.7: itertools.permutations itertools.permutations returns a generator with successive r-length permutations of elements in the iterable. a = [1,2,3] list(itertools.permutations(a)) # [(1, 2, 3), (1, 3, 2), (2, 1, 3), (2, 3, 1), (3, 1, 2), (3, 2, 1)] list(itertools.permutations(a, 2)) [(1, 2), (1, 3), (2, 1), (2, 3), (3, 1), (3, 2)]

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if the list a has duplicate elements, the resulting permutations will have duplicate elements, you can use set to get unique permutations: a = [1,2,1] list(itertools.permutations(a)) # [(1, 2, 1), (1, 1, 2), (2, 1, 1), (2, 1, 1), (1, 1, 2), (1, 2, 1)] set(itertools.permutations(a)) # {(1, 1, 2), (1, 2, 1), (2, 1, 1)}

Section 53.8: itertools.repeat Repeat something n times: >>> import itertools >>> for i in itertools.repeat('over-and-over', 3): ... print(i) over-and-over over-and-over over-and-over

Section 53.9: Get an accumulated sum of numbers in an iterable Python 3.x Version

≥ 3.2

accumulate yields a cumulative sum (or product) of numbers. >>> import itertools as it >>> import operator >>> list(it.accumulate([1,2,3,4,5])) [1, 3, 6, 10, 15] >>> list(it.accumulate([1,2,3,4,5], func=operator.mul)) [1, 2, 6, 24, 120]

Section 53.10: Cycle through elements in an iterator cycle is an infinite iterator. >>> import itertools as it >>> it.cycle('ABCD') A B C D A B C D A B C D ...

Therefore, take care to give boundaries when using this to avoid an infinite loop. Example: >>> # Iterate over each element in cycle for a fixed range >>> cycle_iterator = it.cycle('abc123') >>> [next(cycle_iterator) for i in range(0, 10)] ['a', 'b', 'c', '1', '2', '3', 'a', 'b', 'c', '1']

Section 53.11: itertools.product This function lets you iterate over the Cartesian product of a list of iterables. GoalKicker.com – Python® Notes for Professionals

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For example, for x, y in itertools.product(xrange(10), xrange(10)): print x, y

is equivalent to for x in xrange(10): for y in xrange(10): print x, y

Like all python functions that accept a variable number of arguments, we can pass a list to itertools.product for unpacking, with the * operator. Thus, its = [xrange(10)] * 2 for x,y in itertools.product(*its): print x, y

produces the same results as both of the previous examples. >>> from itertools import product >>> a=[1,2,3,4] >>> b=['a','b','c'] >>> product(a,b)

>>> for i in product(a,b): ... print i ... (1, 'a') (1, 'b') (1, 'c') (2, 'a') (2, 'b') (2, 'c') (3, 'a') (3, 'b') (3, 'c') (4, 'a') (4, 'b') (4, 'c')

Section 53.12: itertools.count Introduction: This simple function generates infinite series of numbers. For example... for number in itertools.count(): if number > 20: break print(number)

Note that we must break or it prints forever! Output: GoalKicker.com – Python® Notes for Professionals

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0 1 2 3 4 5 6 7 8 9 10

Arguments: count() takes two arguments, start and step: for number in itertools.count(start=10, step=4): print(number) if number > 20: break

Output: 10 14 18 22

Section 53.13: Chaining multiple iterators together Use itertools.chain to create a single generator which will yield the values from several generators in sequence. from itertools import chain a = (x for x in ['1', '2', '3', '4']) b = (x for x in ['x', 'y', 'z']) ' '.join(chain(a, b))

Results in: '1 2 3 4 x y z'

As an alternate constructor, you can use the classmethod chain.from_iterable which takes as its single parameter an iterable of iterables. To get the same result as above: ' '.join(chain.from_iterable([a,b])

While chain can take an arbitrary number of arguments, chain.from_iterable is the only way to chain an infinite number of iterables.

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Chapter 54: Asyncio Module Section 54.1: Coroutine and Delegation Syntax Before Python 3.5+ was released, the asyncio module used generators to mimic asynchronous calls and thus had a different syntax than the current Python 3.5 release. Python 3.x Version

≥ 3.5

Python 3.5 introduced the async and await keywords. Note the lack of parentheses around the await func() call. import asyncio async def main(): print(await func()) async def func(): # Do time intensive stuff... return "Hello, world!" if __name__ == "__main__": loop = asyncio.get_event_loop() loop.run_until_complete(main())

Python 3.x Version

≥ 3.3 Version < 3.5

Before Python 3.5, the @asyncio.coroutine decorator was used to define a coroutine. The yield from expression was used for generator delegation. Note the parentheses around the yield from func(). import asyncio @asyncio.coroutine def main(): print((yield from func())) @asyncio.coroutine def func(): # Do time intensive stuff.. return "Hello, world!" if __name__ == "__main__": loop = asyncio.get_event_loop() loop.run_until_complete(main())

Python 3.x Version

≥ 3.5

Here is an example that shows how two functions can be run asynchronously: import asyncio async def cor1(): print("cor1 start") for i in range(10): await asyncio.sleep(1.5) print("cor1", i) async def cor2(): print("cor2 start") for i in range(15): await asyncio.sleep(1)

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print("cor2", i) loop = asyncio.get_event_loop() cors = asyncio.wait([cor1(), cor2()]) loop.run_until_complete(cors)

Section 54.2: Asynchronous Executors Note: Uses the Python 3.5+ async/await syntax asyncio supports the use of Executor objects found in concurrent.futures for scheduling tasks asynchronously.

Event loops have the function run_in_executor() which takes an Executor object, a Callable, and the Callable's parameters. Scheduling a task for an Executor import asyncio from concurrent.futures import ThreadPoolExecutor def func(a, b): # Do time intensive stuff... return a + b async def main(loop): executor = ThreadPoolExecutor() result = await loop.run_in_executor(executor, func, "Hello,", " world!") print(result) if __name__ == "__main__": loop = asyncio.get_event_loop() loop.run_until_complete(main(loop))

Each event loop also has a "default" Executor slot that can be assigned to an Executor. To assign an Executor and schedule tasks from the loop you use the set_default_executor() method. import asyncio from concurrent.futures import ThreadPoolExecutor def func(a, b): # Do time intensive stuff... return a + b async def main(loop): # NOTE: Using `None` as the first parameter designates the `default` Executor. result = await loop.run_in_executor(None, func, "Hello,", " world!") print(result) if __name__ == "__main__": loop = asyncio.get_event_loop() loop.set_default_executor(ThreadPoolExecutor()) loop.run_until_complete(main(loop))

There are two main types of Executor in concurrent.futures, the ThreadPoolExecutor and the ProcessPoolExecutor. The ThreadPoolExecutor contains a pool of threads which can either be manually set to a

specific number of threads through the constructor or defaults to the number of cores on the machine times 5. The ThreadPoolExecutor uses the pool of threads to execute tasks assigned to it and is generally better at CPU-bound

operations rather than I/O bound operations. Contrast that to the ProcessPoolExecutor which spawns a new process for each task assigned to it. The ProcessPoolExecutor can only take tasks and parameters that are GoalKicker.com – Python® Notes for Professionals

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picklable. The most common non-picklable tasks are the methods of objects. If you must schedule an object's method as a task in an Executor you must use a ThreadPoolExecutor.

Section 54.3: Using UVLoop uvloop is an implementation for the asyncio.AbstractEventLoop based on libuv (Used by nodejs). It is compliant

with 99% of asyncio features and is much faster than the traditional asyncio.EventLoop. uvloop is currently not available on Windows, install it with pip install uvloop. import asyncio import uvloop if __name__ == "__main__": asyncio.set_event_loop(uvloop.new_event_loop()) # Do your stuff here ...

One can also change the event loop factory by setting the EventLoopPolicy to the one in uvloop. import asyncio import uvloop if __name__ == "__main__": asyncio.set_event_loop_policy(uvloop.EventLoopPolicy()) loop = asyncio.new_event_loop()

Section 54.4: Synchronization Primitive: Event Concept Use an Event to synchronize the scheduling of multiple coroutines. Put simply, an event is like the gun shot at a running race: it lets the runners off the starting blocks. Example import asyncio # event trigger function def trigger(event): print('EVENT SET') event.set() # wake up coroutines waiting # event consumers async def consumer_a(event): consumer_name = 'Consumer A' print('{} waiting'.format(consumer_name)) await event.wait() print('{} triggered'.format(consumer_name)) async def consumer_b(event): consumer_name = 'Consumer B' print('{} waiting'.format(consumer_name)) await event.wait() print('{} triggered'.format(consumer_name)) # event event = asyncio.Event() # wrap coroutines in one future

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main_future = asyncio.wait([consumer_a(event), consumer_b(event)]) # event loop event_loop = asyncio.get_event_loop() event_loop.call_later(0.1, functools.partial(trigger, event))

# trigger event in 0.1 sec

# complete main_future done, pending = event_loop.run_until_complete(main_future)

Output: Consumer B waiting Consumer A waiting EVENT SET Consumer B triggered Consumer A triggered

Section 54.5: A Simple Websocket Here we make a simple echo websocket using asyncio. We define coroutines for connecting to a server and sending/receiving messages. The communications of the websocket are run in a main coroutine, which is run by an event loop. This example is modified from a prior post. import asyncio import aiohttp session = aiohttp.ClientSession() class EchoWebsocket:

# handles the context manager

async def connect(self): self.websocket = await session.ws_connect("wss://echo.websocket.org") async def send(self, message): self.websocket.send_str(message) async def receive(self): result = (await self.websocket.receive()) return result.data async def main(): echo = EchoWebsocket() await echo.connect() await echo.send("Hello World!") print(await echo.receive())

# "Hello World!"

if __name__ == '__main__': # The main loop loop = asyncio.get_event_loop() loop.run_until_complete(main())

Section 54.6: Common Misconception about asyncio probably the most common misconception about asnycio is that it lets you run any task in parallel - sidestepping the GIL (global interpreter lock) and therefore execute blocking jobs in parallel (on separate threads). it does not! GoalKicker.com – Python® Notes for Professionals

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asyncio (and libraries that are built to collaborate with asyncio) build on coroutines: functions that (collaboratively)

yield the control flow back to the calling function. note asyncio.sleep in the examples above. this is an example of a non-blocking coroutine that waits 'in the background' and gives the control flow back to the calling function (when called with await). time.sleep is an example of a blocking function. the execution flow of the program will just stop there and only return after time.sleep has finished. a real-live example is the requests library which consists (for the time being) on blocking functions only. there is no concurrency if you call any of its functions within asyncio. aiohttp on the other hand was built with asyncio in mind. its coroutines will run concurrently. if you have long-running CPU-bound tasks you would like to run in parallel asyncio is not for you. for that you need threads or multiprocessing. if you have IO-bound jobs running, you may run them concurrently using asyncio.

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Chapter 55: Random module Section 55.1: Creating a random user password In order to create a random user password we can use the symbols provided in the string module. Specifically punctuation for punctuation symbols, ascii_letters for letters and digits for digits: from string import punctuation, ascii_letters, digits

We can then combine all these symbols in a name named symbols: symbols = ascii_letters + digits + punctuation

Remove either of these to create a pool of symbols with fewer elements. After this, we can use random.SystemRandom to generate a password. For a 10 length password: secure_random = random.SystemRandom() password = "".join(secure_random.choice(symbols) for i in range(10)) print(password) # '^@g;J?]M6e'

Note that other routines made immediately available by the random module — such as random.choice, random.randint, etc. — are unsuitable for cryptographic purposes.

Behind the curtains, these routines use the Mersenne Twister PRNG, which does not satisfy the requirements of a CSPRNG. Thus, in particular, you should not use any of them to generate passwords you plan to use. Always use an instance of SystemRandom as shown above. Python 3.x Version

≥ 3.6

Starting from Python 3.6, the secrets module is available, which exposes cryptographically safe functionality. Quoting the official documentation, to generate "a ten-character alphanumeric password with at least one lowercase character, at least one uppercase character, and at least three digits," you could: import string alphabet = string.ascii_letters + string.digits while True: password = ''.join(choice(alphabet) for i in range(10)) if (any(c.islower() for c in password) and any(c.isupper() for c in password) and sum(c.isdigit() for c in password) >= 3): break

Section 55.2: Create cryptographically secure random numbers By default the Python random module use the Mersenne Twister PRNG to generate random numbers, which, although suitable in domains like simulations, fails to meet security requirements in more demanding environments. In order to create a cryptographically secure pseudorandom number, one can use SystemRandom which, by using os.urandom, is able to act as a Cryptographically secure pseudorandom number generator, CPRNG.

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The easiest way to use it simply involves initializing the SystemRandom class. The methods provided are similar to the ones exported by the random module. from random import SystemRandom secure_rand_gen = SystemRandom()

In order to create a random sequence of 10 ints in range [0, 20], one can simply call randrange(): print([secure_rand_gen.randrange(10) for i in range(10)]) # [9, 6, 9, 2, 2, 3, 8, 0, 9, 9]

To create a random integer in a given range, one can use randint: print(secure_rand_gen.randint(0, 20)) # 5

and, accordingly for all other methods. The interface is exactly the same, the only change is the underlying number generator. You can also use os.urandom directly to obtain cryptographically secure random bytes.

Section 55.3: Random and sequences: shue, choice and sample import random

shuffle() You can use random.shuffle() to mix up/randomize the items in a mutable and indexable sequence. For example a list: laughs = ["Hi", "Ho", "He"] random.shuffle(laughs) print(laughs) # Out: ["He", "Hi", "Ho"]

# Shuffles in-place! Don't do: laughs = random.shuffle(laughs)

# Output may vary!

choice() Takes a random element from an arbitrary sequence: print(random.choice(laughs)) # Out: He # Output may vary!

sample() Like choice it takes random elements from an arbitrary sequence but you can specify how many: # |--sequence--|--number--| print(random.sample( laughs , 1 )) # Take one element # Out: ['Ho'] # Output may vary!

it will not take the same element twice: print(random.sample(laughs, 3)) # Take 3 random element from the sequence. # Out: ['Ho', 'He', 'Hi'] # Output may vary!

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print(random.sample(laughs, 4))

# Take 4 random element from the 3-item sequence.

ValueError: Sample larger than population

Section 55.4: Creating random integers and floats: randint, randrange, random, and uniform import random

randint() Returns a random integer between x and y (inclusive): random.randint(x, y)

For example getting a random number between 1 and 8: random.randint(1, 8) # Out: 8

randrange() random.randrange has the same syntax as range and unlike random.randint, the last value is not inclusive: random.randrange(100) # Random integer between 0 and 99 random.randrange(20, 50) # Random integer between 20 and 49 random.rangrange(10, 20, 3) # Random integer between 10 and 19 with step 3 (10, 13, 16 and 19)

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random Returns a random floating point number between 0 and 1: random.random() # Out: 0.66486093215306317

uniform Returns a random floating point number between x and y (inclusive): random.uniform(1, 8) # Out: 3.726062641730108

Section 55.5: Reproducible random numbers: Seed and State Setting a specific Seed will create a fixed random-number series: random.seed(5) # Create a fixed state print(random.randrange(0, 10)) # Get a random integer between 0 and 9 # Out: 9 print(random.randrange(0, 10)) # Out: 4

Resetting the seed will create the same "random" sequence again: random.seed(5) # Reset the random module to the same fixed state. print(random.randrange(0, 10))

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# Out: 9 print(random.randrange(0, 10)) # Out: 4

Since the seed is fixed these results are always 9 and 4. If having specific numbers is not required only that the values will be the same one can also just use getstate and setstate to recover to a previous state: save_state = random.getstate() print(random.randrange(0, 10)) # Out: 5 print(random.randrange(0, 10)) # Out: 8

# Get the current state

random.setstate(save_state) print(random.randrange(0, 10)) # Out: 5 print(random.randrange(0, 10)) # Out: 8

# Reset to saved state

To pseudo-randomize the sequence again you seed with None: random.seed(None)

Or call the seed method with no arguments: random.seed()

Section 55.6: Random Binary Decision import random probability = 0.3 if random.random() < probability: print("Decision with probability 0.3") else: print("Decision with probability 0.7")

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Chapter 56: Functools Module Section 56.1: partial The partial function creates partial function application from another function. It is used to bind values to some of the function's arguments (or keyword arguments) and produce a callable without the already defined arguments. >>> from functools import partial >>> unhex = partial(int, base=16) >>> unhex.__doc__ = 'Convert base16 string to int' >>> unhex('ca11ab1e') 3390155550 partial(), as the name suggests, allows a partial evaluation of a function. Let's look at following example: In [2]: from functools import partial In [3]: def f(a, b, c, x): ...: return 1000*a + 100*b + 10*c + x ...: In [4]: g = partial(f, 1, 1, 1) In [5]: print g(2) 1112

When g is created, f, which takes four arguments(a, b, c, x), is also partially evaluated for the first three arguments, a, b, c,. Evaluation of f is completed when g is called, g(2), which passes the fourth argument to f. One way to think of partial is a shift register; pushing in one argument at the time into some function. partial comes handy for cases where data is coming in as stream and we cannot pass more than one argument.

Section 56.2: cmp_to_key Python changed its sorting methods to accept a key function. Those functions take a value and return a key which is used to sort the arrays. Old comparison functions used to take two values and return -1, 0 or +1 if the first argument is small, equal or greater than the second argument respectively. This is incompatible to the new key-function. That's where functools.cmp_to_key comes in: >>> import functools >>> import locale >>> sorted(["A", "S", "F", "D"], key=functools.cmp_to_key(locale.strcoll)) ['A', 'D', 'F', 'S']

Example taken and adapted from the Python Standard Library Documentation.

Section 56.3: lru_cache The @lru_cache decorator can be used wrap an expensive, computationally-intensive function with a Least Recently Used cache. This allows function calls to be memoized, so that future calls with the same parameters can return instantly instead of having to be recomputed.

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@lru_cache(maxsize=None) # Boundless cache def fibonacci(n): if n < 2: return n return fibonacci(n-1) + fibonacci(n-2) >>> fibonacci(15)

In the example above, the value of fibonacci(3) is only calculated once, whereas if fibonacci didn't have an LRU cache, fibonacci(3) would have been computed upwards of 230 times. Hence, @lru_cache is especially great for recursive functions or dynamic programming, where an expensive function could be called multiple times with the same exact parameters. @lru_cache has two arguments maxsize: Number of calls to save. When the number of unique calls exceeds maxsize, the LRU cache will

remove the least recently used calls. typed (added in 3.3): Flag for determining if equivalent arguments of different types belong to different cache

records (i.e. if 3.0 and 3 count as different arguments) We can see cache stats too: >>> fib.cache_info() CacheInfo(hits=13, misses=16, maxsize=None, currsize=16)

NOTE: Since @lru_cache uses dictionaries to cache results, all parameters for the function must be hashable for the cache to work. Official Python docs for @lru_cache. @lru_cache was added in 3.2.

Section 56.4: total_ordering When we want to create an orderable class, normally we need to define the methods __eq()__, __lt__(), __le__(), __gt__() and __ge__().

The total_ordering decorator, applied to a class, permits the definition of __eq__() and only one between __lt__(), __le__(), __gt__() and __ge__(), and still allow all the ordering operations on the class. @total_ordering class Employee: ... def __eq__(self, other): return ((self.surname, self.name) == (other.surname, other.name)) def __lt__(self, other): return ((self.surname, self.name) < (other.surname, other.name))

The decorator uses a composition of the provided methods and algebraic operations to derive the other comparison methods. For example if we defined __lt__() and __eq()__ and we want to derive __gt__(), we can simply check not __lt__() and not __eq()__. Note: The total_ordering function is only available since Python 2.7.

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Section 56.5: reduce In Python 3.x, the reduce function already explained here has been removed from the built-ins and must now be imported from functools. from functools import reduce def factorial(n): return reduce(lambda a, b: (a*b), range(1, n+1))

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Chapter 57: The dis module Section 57.1: What is Python bytecode? Python is a hybrid interpreter. When running a program, it first assembles it into bytecode which can then be run in the Python interpreter (also called a Python virtual machine). The dis module in the standard library can be used to make the Python bytecode human-readable by disassembling classes, methods, functions, and code objects. >>> def hello(): ... print "Hello, World" ... >>> dis.dis(hello) 2 0 LOAD_CONST 3 PRINT_ITEM 4 PRINT_NEWLINE 5 LOAD_CONST 8 RETURN_VALUE

1 ('Hello, World')

0 (None)

The Python interpreter is stack-based and uses a first-in last-out system. Each operation code (opcode) in the Python assembly language (the bytecode) takes a fixed number of items from the stack and returns a fixed number of items to the stack. If there aren't enough items on the stack for an opcode, the Python interpreter will crash, possibly without an error message.

Section 57.2: Constants in the dis module EXTENDED_ARG = 145 # All opcodes greater than this have 2 operands HAVE_ARGUMENT = 90 # All opcodes greater than this have at least 1 operands cmp_op = ('=', 'in', 'not in', 'is', 'is ... # A list of comparator id's. The indices are used as operands in some opcodes # All opcodes in these lists have the respective types as there operands hascompare = [107] hasconst = [100] hasfree = [135, 136, 137] hasjabs = [111, 112, 113, 114, 115, 119] hasjrel = [93, 110, 120, 121, 122, 143] haslocal = [124, 125, 126] hasname = [90, 91, 95, 96, 97, 98, 101, 106, 108, 109, 116] # A map of opcodes to ids opmap = {'BINARY_ADD': 23, 'BINARY_AND': 64, 'BINARY_DIVIDE': 21, 'BIN... # A map of ids to opcodes opname = ['STOP_CODE', 'POP_TOP', 'ROT_TWO', 'ROT_THREE', 'DUP_TOP', '...

Section 57.3: Disassembling modules To disassemble a Python module, first this has to be turned into a .pyc file (Python compiled). To do this, run python -m compileall .py

Then in an interpreter, run import dis import marshal

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with open(".pyc", "rb") as code_f: code_f.read(8) # Magic number and modification time code = marshal.load(code_f) # Returns a code object which can be disassembled dis.dis(code) # Output the disassembly

This will compile a Python module and output the bytecode instructions with dis. The module is never imported so it is safe to use with untrusted code.

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Chapter 58: The base64 Module Parameter

Description

base64.b64encode(s, altchars=None)

s

A bytes-like object

altchars

A bytes-like object of length 2+ of characters to replace the '+' and '=' characters when creating the Base64 alphabet. Extra characters are ignored.

base64.b64decode(s, altchars=None, validate=False)

s

A bytes-like object

altchars

A bytes-like object of length 2+ of characters to replace the '+' and '=' characters when creating the Base64 alphabet. Extra characters are ignored.

validate

If validate is True, the characters not in the normal Base64 alphabet or the alternative alphabet are not discarded before the padding check

base64.standard_b64encode(s)

s

A bytes-like object

base64.standard_b64decode(s)

s

A bytes-like object

base64.urlsafe_b64encode(s)

s

A bytes-like object

base64.urlsafe_b64decode(s)

s

A bytes-like object

b32encode(s)

s

A bytes-like object

b32decode(s)

s

A bytes-like object

base64.b16encode(s)

s

A bytes-like object

base64.b16decode(s)

s

A bytes-like object

base64.a85encode(b, foldspaces=False, wrapcol=0, pad=False, adobe=False)

b

A bytes-like object

foldspaces

If foldspaces is True, the character 'y' will be used instead of 4 consecutive spaces.

wrapcol

The number characters before a newline (0 implies no newlines)

pad

If pad is True, the bytes are padded to a multiple of 4 before encoding

adobe

If adobe is True, the encoded sequenced with be framed with '' as used with Adobe products

ignorechars

A bytes-like object of characters to ignore in the encoding process

base64.b85encode(b, pad=False)

b

A bytes-like object

pad

If pad is True, the bytes are padded to a multiple of 4 before encoding

base64.b85decode(b)

b

A bytes-like object

Base 64 encoding represents a common scheme for encoding binary into ASCII string format using radix 64. The base64 module is part of the standard library, which means it installs along with Python. Understanding of bytes and strings is critical to this topic and can be reviewed here. This topic explains how to use the various features and number bases of the base64 module.

Section 58.1: Encoding and Decoding Base64 To include the base64 module in your script, you must import it first: import base64

The base64 encode and decode functions both require a bytes-like object. To get our string into bytes, we must encode it using Python's built in encode function. Most commonly, the UTF-8 encoding is used, however a full list of these standard encodings (including languages with different characters) can be found here in the official Python Documentation. Below is an example of encoding a string into bytes: s = "Hello World!" b = s.encode("UTF-8")

The output of the last line would be: b'Hello World!'

The b prefix is used to denote the value is a bytes object. To Base64 encode these bytes, we use the base64.b64encode() function: import base64 s = "Hello World!" b = s.encode("UTF-8") e = base64.b64encode(b) print(e)

That code would output the following: b'SGVsbG8gV29ybGQh'

which is still in the bytes object. To get a string out of these bytes, we can use Python's decode() method with the UTF-8 encoding: import base64 s = "Hello World!" b = s.encode("UTF-8") e = base64.b64encode(b)

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s1 = e.decode("UTF-8") print(s1)

The output would then be: SGVsbG8gV29ybGQh

If we wanted to encode the string and then decode we could use the base64.b64decode() method: import base64 # Creating a string s = "Hello World!" # Encoding the string into bytes b = s.encode("UTF-8") # Base64 Encode the bytes e = base64.b64encode(b) # Decoding the Base64 bytes to string s1 = e.decode("UTF-8") # Printing Base64 encoded string print("Base64 Encoded:", s1) # Encoding the Base64 encoded string into bytes b1 = s1.encode("UTF-8") # Decoding the Base64 bytes d = base64.b64decode(b1) # Decoding the bytes to string s2 = d.decode("UTF-8") print(s2)

As you may have expected, the output would be the original string: Base64 Encoded: SGVsbG8gV29ybGQh Hello World!

Section 58.2: Encoding and Decoding Base32 The base64 module also includes encoding and decoding functions for Base32. These functions are very similar to the Base64 functions: import base64 # Creating a string s = "Hello World!" # Encoding the string into bytes b = s.encode("UTF-8") # Base32 Encode the bytes e = base64.b32encode(b) # Decoding the Base32 bytes to string s1 = e.decode("UTF-8") # Printing Base32 encoded string print("Base32 Encoded:", s1) # Encoding the Base32 encoded string into bytes b1 = s1.encode("UTF-8") # Decoding the Base32 bytes d = base64.b32decode(b1) # Decoding the bytes to string s2 = d.decode("UTF-8") print(s2)

This would produce the following output: GoalKicker.com – Python® Notes for Professionals

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Base32 Encoded: JBSWY3DPEBLW64TMMQQQ==== Hello World!

Section 58.3: Encoding and Decoding Base16 The base64 module also includes encoding and decoding functions for Base16. Base 16 is most commonly referred to as hexadecimal. These functions are very similar to the both the Base64 and Base32 functions: import base64 # Creating a string s = "Hello World!" # Encoding the string into bytes b = s.encode("UTF-8") # Base16 Encode the bytes e = base64.b16encode(b) # Decoding the Base16 bytes to string s1 = e.decode("UTF-8") # Printing Base16 encoded string print("Base16 Encoded:", s1) # Encoding the Base16 encoded string into bytes b1 = s1.encode("UTF-8") # Decoding the Base16 bytes d = base64.b16decode(b1) # Decoding the bytes to string s2 = d.decode("UTF-8") print(s2)

This would produce the following output: Base16 Encoded: 48656C6C6F20576F726C6421 Hello World!

Section 58.4: Encoding and Decoding ASCII85 Adobe created its own encoding called ASCII85 which is similar to Base85, but has its differences. This encoding is used frequently in Adobe PDF files. These functions were released in Python version 3.4. Otherwise, the functions base64.a85encode() and base64.a85encode() are similar to the previous: import base64 # Creating a string s = "Hello World!" # Encoding the string into bytes b = s.encode("UTF-8") # ASCII85 Encode the bytes e = base64.a85encode(b) # Decoding the ASCII85 bytes to string s1 = e.decode("UTF-8") # Printing ASCII85 encoded string print("ASCII85 Encoded:", s1) # Encoding the ASCII85 encoded string into bytes b1 = s1.encode("UTF-8") # Decoding the ASCII85 bytes d = base64.a85decode(b1) # Decoding the bytes to string s2 = d.decode("UTF-8")

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print(s2)

This outputs the following: ASCII85 Encoded: 87cURD]i,"Ebo80 Hello World!

Section 58.5: Encoding and Decoding Base85 Just like the Base64, Base32, and Base16 functions, the Base85 encoding and decoding functions are base64.b85encode() and base64.b85decode(): import base64 # Creating a string s = "Hello World!" # Encoding the string into bytes b = s.encode("UTF-8") # Base85 Encode the bytes e = base64.b85encode(b) # Decoding the Base85 bytes to string s1 = e.decode("UTF-8") # Printing Base85 encoded string print("Base85 Encoded:", s1) # Encoding the Base85 encoded string into bytes b1 = s1.encode("UTF-8") # Decoding the Base85 bytes d = base64.b85decode(b1) # Decoding the bytes to string s2 = d.decode("UTF-8") print(s2)

which outputs the following: Base85 Encoded: NM&qnZy;B1a%^NF Hello World!

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Chapter 59: Queue Module The Queue module implements multi-producer, multi-consumer queues. It is especially useful in threaded programming when information must be exchanged safely between multiple threads. There are three types of queues provides by queue module,Which are as following : 1. Queue 2. LifoQueue 3. PriorityQueue Exception which could be come: 1. Full (queue overflow) 2. Empty (queue underflow)

Section 59.1: Simple example from Queue import Queue question_queue = Queue() for x in range(1,10): temp_dict = ('key', x) question_queue.put(temp_dict) while(not question_queue.empty()): item = question_queue.get() print(str(item))

Output: ('key', ('key', ('key', ('key', ('key', ('key', ('key', ('key', ('key',

1) 2) 3) 4) 5) 6) 7) 8) 9)

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Chapter 60: Deque Module Parameter iterable

Details Creates the deque with initial elements copied from another iterable.

maxlen

Limits how large the deque can be, pushing out old elements as new are added.

Section 60.1: Basic deque using The main methods that are useful with this class are popleft and appendleft from collections import deque d = deque([1, 2, 3]) p = d.popleft() d.appendleft(5)

# p = 1, d = deque([2, 3]) # d = deque([5, 2, 3])

Section 60.2: Available methods in deque Creating empty deque: dl = deque()

# deque([]) creating empty deque

Creating deque with some elements: dl = deque([1, 2, 3, 4])

# deque([1, 2, 3, 4])

Adding element to deque: dl.append(5)

# deque([1, 2, 3, 4, 5])

Adding element left side of deque: dl.appendleft(0)

# deque([0, 1, 2, 3, 4, 5])

Adding list of elements to deque: dl.extend([6, 7])

# deque([0, 1, 2, 3, 4, 5, 6, 7])

Adding list of elements to from the left side: dl.extendleft([-2, -1])

# deque([-1, -2, 0, 1, 2, 3, 4, 5, 6, 7])

Using .pop() element will naturally remove an item from the right side: dl.pop()

# 7 => deque([-1, -2, 0, 1, 2, 3, 4, 5, 6])

Using .popleft() element to remove an item from the left side: dl.popleft()

# -1 deque([-2, 0, 1, 2, 3, 4, 5, 6])

Remove element by its value:

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dl.remove(1)

# deque([-2, 0, 2, 3, 4, 5, 6])

Reverse the order of the elements in deque: dl.reverse()

# deque([6, 5, 4, 3, 2, 0, -2])

Section 60.3: limit deque size Use the maxlen parameter while creating a deque to limit the size of the deque: from collections import deque d = deque(maxlen=3) # only holds 3 items d.append(1) # deque([1]) d.append(2) # deque([1, 2]) d.append(3) # deque([1, 2, 3]) d.append(4) # deque([2, 3, 4]) (1 is removed because its maxlen is 3)

Section 60.4: Breadth First Search The Deque is the only Python data structure with fast Queue operations. (Note queue.Queue isn't normally suitable, since it's meant for communication between threads.) A basic use case of a Queue is the breadth first search. from collections import deque def bfs(graph, root): distances = {} distances[root] = 0 q = deque([root]) while q: # The oldest seen (but not yet visited) node will be the left most one. current = q.popleft() for neighbor in graph[current]: if neighbor not in distances: distances[neighbor] = distances[current] + 1 # When we see a new node, we add it to the right side of the queue. q.append(neighbor) return distances

Say we have a simple directed graph: graph = {1:[2,3], 2:[4], 3:[4,5], 4:[3,5], 5:[]}

We can now find the distances from some starting position: >>> bfs(graph, 1) {1: 0, 2: 1, 3: 1, 4: 2, 5: 2} >>> bfs(graph, 3) {3: 0, 4: 1, 5: 1}

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Chapter 61: Webbrowser Module Parameter

Details

webbrowser.open()

url

the URL to open in the web browser

new

0 opens the URL in the existing tab, 1 opens in a new window, 2 opens in new tab

autoraise

if set to True, the window will be moved on top of the other windows

webbrowser.open_new()

url

the URL to open in the web browser

webbrowser.open_new_tab()

url

the URL to open in the web browser

webbrowser.get()

using

the browser to use

webbrowser.register()

url

browser name

constructor

path to the executable browser (help)

instance

An instance of a web browser returned from the webbrowser.get() method

According to Python's standard documentation, the webbrowser module provides a high-level interface to allow displaying Web-based documents to users. This topic explains and demonstrates proper usage of the webbrowser module.

Section 61.1: Opening a URL with Default Browser To simply open a URL, use the webbrowser.open() method: import webbrowser webbrowser.open("http://stackoverflow.com")

If a browser window is currently open, the method will open a new tab at the specified URL. If no window is open, the method will open the operating system's default browser and navigate to the URL in the parameter. The open method supports the following parameters: url - the URL to open in the web browser (string) [required] new - 0 opens in existing tab, 1 opens new window, 2 opens new tab (integer) [default 0] autoraise - if set to True, the window will be moved on top of other applications' windows (Boolean)

[default False] Note, the new and autoraise arguments rarely work as the majority of modern browsers refuse these commands. Webbrowser can also try to open URLs in new windows with the open_new method: import webbrowser webbrowser.open_new("http://stackoverflow.com")

This method is commonly ignored by modern browsers and the URL is usually opened in a new tab. Opening a new tab can be tried by the module using the open_new_tab method: import webbrowser webbrowser.open_new_tab("http://stackoverflow.com")

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Section 61.2: Opening a URL with Dierent Browsers The webbrowser module also supports different browsers using the register() and get() methods. The get method is used to create a browser controller using a specific executable's path and the register method is used to attach these executables to preset browser types for future use, commonly when multiple browser types are used. import webbrowser ff_path = webbrowser.get("C:/Program Files/Mozilla Firefox/firefox.exe") ff = webbrowser.get(ff_path) ff.open("http://stackoverflow.com/")

Registering a browser type: import webbrowser ff_path = webbrowser.get("C:/Program Files/Mozilla Firefox/firefox.exe") ff = webbrowser.get(ff_path) webbrowser.register('firefox', None, ff) # Now to refer to use Firefox in the future you can use this webbrowser.get('firefox').open("https://stackoverflow.com/")

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Chapter 62: tkinter Released in Tkinter is Python's most popular GUI (Graphical User Interface) library. This topic explains proper usage of this library and its features.

Section 62.1: Geometry Managers Tkinter has three mechanisms for geometry management: place, pack, and grid. The place manager uses absolute pixel coordinates. The pack manager places widgets into one of 4 sides. New widgets are placed next to existing widgets. The grid manager places widgets into a grid similar to a dynamically resizing spreadsheet. Place The most common keyword arguments for widget.place are as follows: x, the absolute x-coordinate of the widget y, the absolute y-coordinate of the widget height, the absolute height of the widget width, the absolute width of the widget

A code example using place: class PlaceExample(Frame): def __init__(self,master): Frame.__init__(self,master) self.grid() top_text=Label(master,text="This is on top at the origin") #top_text.pack() top_text.place(x=0,y=0,height=50,width=200) bottom_right_text=Label(master,text="This is at position 200,400") #top_text.pack() bottom_right_text.place(x=200,y=400,height=50,width=200) # Spawn Window if __name__=="__main__": root=Tk() place_frame=PlaceExample(root) place_frame.mainloop()

Pack widget.pack can take the following keyword arguments: expand, whether or not to fill space left by parent fill, whether to expand to fill all space (NONE (default), X, Y, or BOTH) side, the side to pack against (TOP (default), BOTTOM, LEFT, or RIGHT)

Grid The most commonly used keyword arguments of widget.grid are as follows: row, the row of the widget (default smallest unoccupied) rowspan, the number of columns a widget spans (default 1) column, the column of the widget (default 0)

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columnspan, the number of columns a widget spans (default 1) sticky, where to place widget if the grid cell is larger than it (combination of N,NE,E,SE,S,SW,W,NW)

The rows and columns are zero indexed. Rows increase going down, and columns increase going right. A code example using grid: from tkinter import * class GridExample(Frame): def __init__(self,master): Frame.__init__(self,master) self.grid() top_text=Label(self,text="This text appears on top left") top_text.grid() # Default position 0, 0 bottom_text=Label(self,text="This text appears on bottom left") bottom_text.grid() # Default position 1, 0 right_text=Label(self,text="This text appears on the right and spans both rows", wraplength=100) # Position is 0,1 # Rowspan means actual position is [0-1],1 right_text.grid(row=0,column=1,rowspan=2) # Spawn Window if __name__=="__main__": root=Tk() grid_frame=GridExample(root) grid_frame.mainloop()

Never mix pack and grid within the same frame! Doing so will lead to application deadlock!

Section 62.2: A minimal tkinter Application tkinter is a GUI toolkit that provides a wrapper around the Tk/Tcl GUI library and is included with Python. The

following code creates a new window using tkinter and places some text in the window body. Note: In Python 2, the capitalization may be slightly different, see Remarks section below.

import tkinter as tk # GUI window is a subclass of the basic tkinter Frame object class HelloWorldFrame(tk.Frame): def __init__(self, master): # Call superclass constructor tk.Frame.__init__(self, master) # Place frame into main window self.grid() # Create text box with "Hello World" text hello = tk.Label(self, text="Hello World! This label can hold strings!") # Place text box into frame hello.grid(row=0, column=0) # Spawn window if __name__ == "__main__": # Create main window object root = tk.Tk() # Set title of window root.title("Hello World!")

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# Instantiate HelloWorldFrame object hello_frame = HelloWorldFrame(root) # Start GUI hello_frame.mainloop()

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Chapter 63: pyautogui module pyautogui is a module used to control mouse and keyboard. This module is basically used to automate mouse click and keyboard press tasks. For the mouse, the coordinates of the screen (0,0) start from the top-left corner. If you are out of control, then quickly move the mouse cursor to top-left, it will take the control of mouse and keyboard from the Python and give it back to you.

Section 63.1: Mouse Functions These are some of useful mouse functions to control the mouse. size() #gave you the size of the screen position() #return current position of mouse moveTo(200,0,duration=1.5) #move the cursor to (200,0) position with 1.5 second delay moveRel() #move the cursor relative to your current position. click(337,46) #it will click on the position mention there dragRel() #it will drag the mouse relative to position pyautogui.displayMousePosition() #gave you the current mouse position but should be done on terminal.

Section 63.2: Keyboard Functions These are some of useful keyboard functions to automate the key pressing. typewrite('') #this will type the string on the screen where current window has focused. typewrite(['a','b','left','left','X','Y']) pyautogui.KEYBOARD_KEYS #get the list of all the keyboard_keys. pyautogui.hotkey('ctrl','o') #for the combination of keys to enter.

Section 63.3: Screenshot And Image Recognition These function will help you to take the screenshot and also match the image with the part of the screen. .screenshot('c:\\path') #get the screenshot. .locateOnScreen('c:\\path') #search that image on screen and get the coordinates for you. locateCenterOnScreen('c:\\path') #get the coordinate for the image on screen.

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Chapter 64: Indexing and Slicing Paramer obj

Description The object that you want to extract a "sub-object" from

start

The index of obj that you want the sub-object to start from (keep in mind that Python is zero-indexed, meaning that the first item of obj has an index of 0). If omitted, defaults to 0.

stop

The (non-inclusive) index of obj that you want the sub-object to end at. If omitted, defaults to len(obj).

step

Allows you to select only every step item. If omitted, defaults to 1.

Section 64.1: Basic Slicing For any iterable (for eg. a string, list, etc), Python allows you to slice and return a substring or sublist of its data. Format for slicing: iterable_name[start:stop:step]

where, start is the first index of the slice. Defaults to 0 (the index of the first element) stop one past the last index of the slice. Defaults to len(iterable) step is the step size (better explained by the examples below)

Examples: a = "abcdef" a # # a[-1] # a[:] # a[::] # a[3:] # a[:4] # a[2:4] #

"abcdef" Same as a[:] or a[::] since it uses the defaults for all three indices "f" "abcdef" "abcdef" "def" (from index 3, to end(defaults to size of iterable)) "abcd" (from beginning(default 0) to position 4 (excluded)) "cd" (from position 2, to position 4 (excluded))

In addition, any of the above can be used with the step size defined: a[::2] a[1:4:2]

# "ace" (every 2nd element) # "bd" (from index 1, to index 4 (excluded), every 2nd element)

Indices can be negative, in which case they're computed from the end of the sequence a[:-1] a[:-2] a[-1:]

# "abcde" (from index 0 (default), to the second last element (last element - 1)) # "abcd" (from index 0 (default), to the third last element (last element -2)) # "f" (from the last element to the end (default len())

Step sizes can also be negative, in which case slice will iterate through the list in reverse order: a[3:1:-1]

# "dc" (from index 2 to None (default), in reverse order)

This construct is useful for reversing an iterable a[::-1]

# "fedcba" (from last element (default len()-1), to first, in reverse order(-1))

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Notice that for negative steps the default end_index is None (see http://stackoverflow.com/a/12521981 ) a[5:None:-1] # "fedcba" (this is equivalent to a[::-1]) a[5:0:-1] # "fedcb" (from the last element (index 5) to second element (index 1)

Section 64.2: Reversing an object You can use slices to very easily reverse a str, list, or tuple (or basically any collection object that implements slicing with the step parameter). Here is an example of reversing a string, although this applies equally to the other types listed above: s = 'reverse me!' s[::-1] # '!em esrever'

Let's quickly look at the syntax. [::-1] means that the slice should be from the beginning until the end of the string (because start and end are omitted) and a step of -1 means that it should move through the string in reverse.

Section 64.3: Slice assignment Another neat feature using slices is slice assignment. Python allows you to assign new slices to replace old slices of a list in a single operation. This means that if you have a list, you can replace multiple members in a single assignment: lst = [1, 2, 3] lst[1:3] = [4, 5] print(lst) # Out: [1, 4, 5]

The assignment shouldn't match in size as well, so if you wanted to replace an old slice with a new slice that is different in size, you could: lst = [1, 2, 3, 4, 5] lst[1:4] = [6] print(lst) # Out: [1, 6, 5]

It's also possible to use the known slicing syntax to do things like replacing the entire list: lst = [1, 2, 3] lst[:] = [4, 5, 6] print(lst) # Out: [4, 5, 6]

Or just the last two members: lst = [1, 2, 3] lst[-2:] = [4, 5, 6] print(lst) # Out: [1, 4, 5, 6]

Section 64.4: Making a shallow copy of an array A quick way to make a copy of an array (as opposed to assigning a variable with another reference to the original array) is: arr[:]

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Let's examine the syntax. [:] means that start, end, and slice are all omitted. They default to 0, len(arr), and 1, respectively, meaning that subarray that we are requesting will have all of the elements of arr from the beginning until the very end. In practice, this looks something like: arr = ['a', 'b', 'c'] copy = arr[:] arr.append('d') print(arr) # ['a', 'b', 'c', 'd'] print(copy) # ['a', 'b', 'c']

As you can see, arr.append('d') added d to arr, but copy remained unchanged! Note that this makes a shallow copy, and is identical to arr.copy().

Section 64.5: Indexing custom classes: __getitem__, __setitem__ and __delitem__ class MultiIndexingList: def __init__(self, value): self.value = value def __repr__(self): return repr(self.value) def __getitem__(self, item): if isinstance(item, (int, slice)): return self.__class__(self.value[item]) return [self.value[i] for i in item] def __setitem__(self, item, value): if isinstance(item, int): self.value[item] = value elif isinstance(item, slice): raise ValueError('Cannot interpret slice with multiindexing') else: for i in item: if isinstance(i, slice): raise ValueError('Cannot interpret slice with multiindexing') self.value[i] = value def __delitem__(self, item): if isinstance(item, int): del self.value[item] elif isinstance(item, slice): del self.value[item] else: if any(isinstance(elem, slice) for elem in item): raise ValueError('Cannot interpret slice with multiindexing') item = sorted(item, reverse=True) for elem in item: del self.value[elem]

This allows slicing and indexing for element access: a = MultiIndexingList([1,2,3,4,5,6,7,8]) a # Out: [1, 2, 3, 4, 5, 6, 7, 8]

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a[1,5,2,6,1] # Out: [2, 6, 3, 7, 2] a[4, 1, 5:, 2, ::2] # Out: [5, 2, [6, 7, 8], 3, [1, 3, 5, 7]] # 4|1-|----50:---|2-|-----::2-----

> 'a'

You can access the second element in the list by index 1, third element by index 2 and so on: print(arr[1]) >> 'b' print(arr[2]) >> 'c'

You can also use negative indices to access elements from the end of the list. eg. index -1 will give you the last element of the list and index -2 will give you the second-to-last element of the list: print(arr[-1]) >> 'd' print(arr[-2]) >> 'c'

If you try to access an index which is not present in the list, an IndexError will be raised: print arr[6] Traceback (most recent call last): File "", line 1, in IndexError: list index out of range

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Chapter 65: Plotting with Matplotlib Matplotlib (https://matplotlib.org/) is a library for 2D plotting based on NumPy. Here are some basic examples. More examples can be found in the official documentation (https://matplotlib.org/2.0.2/gallery.html and https://matplotlib.org/2.0.2/examples/index.html)

Section 65.1: Plots with Common X-axis but dierent Y-axis : Using twinx() In this example, we will plot a sine curve and a hyperbolic sine curve in the same plot with a common x-axis having different y-axis. This is accomplished by the use of twinx() command. # # # # # #

Plotting tutorials in Python Adding Multiple plots by twin x axis Good for plots having different y axis range Separate axes and figure objects replicate axes object and plot curves use axes to set attributes

# Note: # Grid for second curve unsuccessful : let me know if you find it! :( import numpy as np import matplotlib.pyplot as plt x = np.linspace(0, 2.0*np.pi, 101) y = np.sin(x) z = np.sinh(x) # separate the figure object and axes object # from the plotting object fig, ax1 = plt.subplots() # Duplicate the axes with a different y axis # and the same x axis ax2 = ax1.twinx() # ax2 and ax1 will have common x axis and different y axis # plot the curves on axes 1, and 2, and get the curve handles curve1, = ax1.plot(x, y, label="sin", color='r') curve2, = ax2.plot(x, z, label="sinh", color='b') # Make a curves list to access the parameters in the curves curves = [curve1, curve2] # add legend via axes 1 or axes 2 object. # one command is usually sufficient # ax1.legend() # will not display the legend of ax2 # ax2.legend() # will not display the legend of ax1 ax1.legend(curves, [curve.get_label() for curve in curves]) # ax2.legend(curves, [curve.get_label() for curve in curves]) # also valid # Global figure properties plt.title("Plot of sine and hyperbolic sine") plt.show()

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Section 65.2: Plots with common Y-axis and dierent X-axis using twiny() In this example, a plot with curves having common y-axis but different x-axis is demonstrated using twiny() method. Also, some additional features such as the title, legend, labels, grids, axis ticks and colours are added to the plot. # # # # # #

Plotting tutorials in Python Adding Multiple plots by twin y axis Good for plots having different x axis range Separate axes and figure objects replicate axes object and plot curves use axes to set attributes

import numpy as np import matplotlib.pyplot as plt y = np.linspace(0, 2.0*np.pi, 101) x1 = np.sin(y) x2 = np.sinh(y) # values for making ticks in x and y axis ynumbers = np.linspace(0, 7, 15) xnumbers1 = np.linspace(-1, 1, 11) xnumbers2 = np.linspace(0, 300, 7) # separate the figure object and axes object # from the plotting object fig, ax1 = plt.subplots()

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# Duplicate the axes with a different x axis # and the same y axis ax2 = ax1.twiny() # ax2 and ax1 will have common y axis and different x axis # plot the curves on axes 1, and 2, and get the axes handles curve1, = ax1.plot(x1, y, label="sin", color='r') curve2, = ax2.plot(x2, y, label="sinh", color='b') # Make a curves list to access the parameters in the curves curves = [curve1, curve2] # add legend via axes 1 or axes 2 object. # one command is usually sufficient # ax1.legend() # will not display the legend of ax2 # ax2.legend() # will not display the legend of ax1 # ax1.legend(curves, [curve.get_label() for curve in curves]) ax2.legend(curves, [curve.get_label() for curve in curves]) # also valid # x axis labels via the axes ax1.set_xlabel("Magnitude", color=curve1.get_color()) ax2.set_xlabel("Magnitude", color=curve2.get_color()) # y axis label via the axes ax1.set_ylabel("Angle/Value", color=curve1.get_color()) # ax2.set_ylabel("Magnitude", color=curve2.get_color()) # does not work # ax2 has no property control over y axis # y ticks - make them coloured as well ax1.tick_params(axis='y', colors=curve1.get_color()) # ax2.tick_params(axis='y', colors=curve2.get_color()) # does not work # ax2 has no property control over y axis # x axis ticks via the axes ax1.tick_params(axis='x', colors=curve1.get_color()) ax2.tick_params(axis='x', colors=curve2.get_color()) # set x ticks ax1.set_xticks(xnumbers1) ax2.set_xticks(xnumbers2) # set y ticks ax1.set_yticks(ynumbers) # ax2.set_yticks(ynumbers) # also works # Grids via axes 1 # use this if axes 1 is used to # define the properties of common x axis # ax1.grid(color=curve1.get_color()) # To make grids using axes 2 ax1.grid(color=curve2.get_color()) ax2.grid(color=curve2.get_color()) ax1.xaxis.grid(False) # Global figure properties plt.title("Plot of sine and hyperbolic sine") plt.show()

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Section 65.3: A Simple Plot in Matplotlib This example illustrates how to create a simple sine curve using Matplotlib # Plotting tutorials in Python # Launching a simple plot import numpy as np import matplotlib.pyplot as plt # angle varying between 0 and 2*pi x = np.linspace(0, 2.0*np.pi, 101) y = np.sin(x)

# sine function

plt.plot(x, y) plt.show()

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Section 65.4: Adding more features to a simple plot : axis labels, title, axis ticks, grid, and legend In this example, we take a sine curve plot and add more features to it; namely the title, axis labels, title, axis ticks, grid and legend. # Plotting tutorials in Python # Enhancing a plot import numpy as np import matplotlib.pyplot as plt x = np.linspace(0, 2.0*np.pi, 101) y = np.sin(x) # values for making ticks in x and y axis xnumbers = np.linspace(0, 7, 15) ynumbers = np.linspace(-1, 1, 11) plt.plot(x, y, color='r', label='sin') # r - red colour plt.xlabel("Angle in Radians") plt.ylabel("Magnitude") plt.title("Plot of some trigonometric functions") plt.xticks(xnumbers) plt.yticks(ynumbers) plt.legend() plt.grid() plt.axis([0, 6.5, -1.1, 1.1]) # [xstart, xend, ystart, yend] plt.show()

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Section 65.5: Making multiple plots in the same figure by superimposition similar to MATLAB In this example, a sine curve and a cosine curve are plotted in the same figure by superimposing the plots on top of each other. # # # #

Plotting tutorials in Python Adding Multiple plots by superimposition Good for plots sharing similar x, y limits Using single plot command and legend

import numpy as np import matplotlib.pyplot as plt x = np.linspace(0, 2.0*np.pi, 101) y = np.sin(x) z = np.cos(x) # values for making ticks in x and y axis xnumbers = np.linspace(0, 7, 15) ynumbers = np.linspace(-1, 1, 11) plt.plot(x, y, 'r', x, z, 'g') # r, g - red, green colour plt.xlabel("Angle in Radians") plt.ylabel("Magnitude") plt.title("Plot of some trigonometric functions") plt.xticks(xnumbers) plt.yticks(ynumbers) plt.legend(['sine', 'cosine']) plt.grid()

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plt.axis([0, 6.5, -1.1, 1.1]) # [xstart, xend, ystart, yend] plt.show()

Section 65.6: Making multiple Plots in the same figure using plot superimposition with separate plot commands Similar to the previous example, here, a sine and a cosine curve are plotted on the same figure using separate plot commands. This is more Pythonic and can be used to get separate handles for each plot. # # # # #

Plotting tutorials in Python Adding Multiple plots by superimposition Good for plots sharing similar x, y limits Using multiple plot commands Much better and preferred than previous

import numpy as np import matplotlib.pyplot as plt x = np.linspace(0, 2.0*np.pi, 101) y = np.sin(x) z = np.cos(x) # values for making ticks in x and y axis xnumbers = np.linspace(0, 7, 15) ynumbers = np.linspace(-1, 1, 11) plt.plot(x, y, color='r', label='sin') # r - red colour plt.plot(x, z, color='g', label='cos') # g - green colour plt.xlabel("Angle in Radians") plt.ylabel("Magnitude")

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plt.title("Plot of some trigonometric functions") plt.xticks(xnumbers) plt.yticks(ynumbers) plt.legend() plt.grid() plt.axis([0, 6.5, -1.1, 1.1]) # [xstart, xend, ystart, yend] plt.show()

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Chapter 66: graph-tool The python tools can be used to generate graph

Section 66.1: PyDotPlus PyDotPlus is an improved version of the old pydot project that provides a Python Interface to Graphviz’s Dot language. Installation For the latest stable version: pip install pydotplus

For the development version: pip install https://github.com/carlos-jenkins/pydotplus/archive/master.zip

Load graph as defined by a DOT file The file is assumed to be in DOT format. It will be loaded, parsed and a Dot class will be returned, representing the graph. For example, a simple demo.dot: digraph demo1{ a -> b -> c; c ->a; }

import pydotplus graph_a = pydotplus.graph_from_dot_file('demo.dot') graph_a.write_svg('test.svg') # generate graph in svg.

You will get a svg(Scalable Vector Graphics) like this:

Section 66.2: PyGraphviz Get PyGraphviz from the Python Package Index at http://pypi.python.org/pypi/pygraphviz or install it with: pip install pygraphviz

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and an attempt will be made to find and install an appropriate version that matches your operating system and Python version. You can install the development version (at github.com) with: pip install git://github.com/pygraphviz/pygraphviz.git#egg=pygraphviz

Get PyGraphviz from the Python Package Index at http://pypi.python.org/pypi/pygraphviz or install it with: easy_install pygraphviz

and an attempt will be made to find and install an appropriate version that matches your operating system and Python version. Load graph as defined by a DOT file The file is assumed to be in DOT format. It will be loaded, parsed and a Dot class will be returned, representing the graph. For example,a simple demo.dot: digraph demo1{ a -> b -> c; c ->a; } Load it and draw it. import pygraphviz as pgv G = pgv.AGraph("demo.dot") G.draw('test', format='svg', prog='dot')

You will get a svg(Scalable Vector Graphics) like this:

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Chapter 67: Generators Generators are lazy iterators created by generator functions (using yield) or generator expressions (using (an_expression for x in an_iterator)).

Section 67.1: Introduction Generator expressions are similar to list, dictionary and set comprehensions, but are enclosed with parentheses. The parentheses do not have to be present when they are used as the sole argument for a function call. expression = (x**2 for x in range(10))

This example generates the 10 first perfect squares, including 0 (in which x = 0). Generator functions are similar to regular functions, except that they have one or more yield statements in their body. Such functions cannot return any values (however empty returns are allowed if you want to stop the generator early). def function(): for x in range(10): yield x**2

This generator function is equivalent to the previous generator expression, it outputs the same. Note: all generator expressions have their own equivalent functions, but not vice versa. A generator expression can be used without parentheses if both parentheses would be repeated otherwise: sum(i for i in range(10) if i % 2 == 0) any(x = 0 for x in foo) type(a > b for a in foo if a % 2 == 1)

#Output: 20 #Output: True or False depending on foo #Output:

Instead of: sum((i for i in range(10) if i % 2 == 0)) any((x = 0 for x in foo)) type((a > b for a in foo if a % 2 == 1))

But not: fooFunction(i for i in range(10) if i % 2 == 0,foo,bar) return x = 0 for x in foo barFunction(baz, a > b for a in foo if a % 2 == 1)

Calling a generator function produces a generator object, which can later be iterated over. Unlike other types of iterators, generator objects may only be traversed once. g1 = function() print(g1) # Out:

Notice that a generator's body is not immediately executed: when you call function() in the example above, it immediately returns a generator object, without executing even the first print statement. This allows generators to consume less memory than functions that return a list, and it allows creating generators that produce infinitely long sequences. GoalKicker.com – Python® Notes for Professionals

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For this reason, generators are often used in data science, and other contexts involving large amounts of data. Another advantage is that other code can immediately use the values yielded by a generator, without waiting for the complete sequence to be produced. However, if you need to use the values produced by a generator more than once, and if generating them costs more than storing, it may be better to store the yielded values as a list than to re-generate the sequence. See 'Resetting a generator' below for more details. Typically a generator object is used in a loop, or in any function that requires an iterable: for x in g1: print("Received", x) # # # # # # # # # # #

Output: Received Received Received Received Received Received Received Received Received Received

0 1 4 9 16 25 36 49 64 81

arr1 = list(g1) # arr1 = [], because the loop above already consumed all the values. g2 = function() arr2 = list(g2) # arr2 = [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Since generator objects are iterators, one can iterate over them manually using the next() function. Doing so will return the yielded values one by one on each subsequent invocation. Under the hood, each time you call next() on a generator, Python executes statements in the body of the generator function until it hits the next yield statement. At this point it returns the argument of the yield command, and remembers the point where that happened. Calling next() once again will resume execution from that point and continue until the next yield statement. If Python reaches the end of the generator function without encountering any more yields, a StopIteration exception is raised (this is normal, all iterators behave in the same way). g3 = function() a = next(g3) # b = next(g3) # c = next(g3) # ... j = next(g3) #

a becomes 0 b becomes 1 c becomes 2 Raises StopIteration, j remains undefined

Note that in Python 2 generator objects had .next() methods that could be used to iterate through the yielded values manually. In Python 3 this method was replaced with the .__next__() standard for all iterators. Resetting a generator Remember that you can only iterate through the objects generated by a generator once. If you have already iterated through the objects in a script, any further attempt do so will yield None. If you need to use the objects generated by a generator more than once, you can either define the generator GoalKicker.com – Python® Notes for Professionals

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function again and use it a second time, or, alternatively, you can store the output of the generator function in a list on first use. Re-defining the generator function will be a good option if you are dealing with large volumes of data, and storing a list of all data items would take up a lot of disc space. Conversely, if it is costly to generate the items initially, you may prefer to store the generated items in a list so that you can re-use them.

Section 67.2: Infinite sequences Generators can be used to represent infinite sequences: def integers_starting_from(n): while True: yield n n += 1 natural_numbers = integers_starting_from(1)

Infinite sequence of numbers as above can also be generated with the help of itertools.count. The above code could be written as below natural_numbers = itertools.count(1)

You can use generator comprehensions on infinite generators to produce new generators: multiples_of_two = (x * 2 for x in natural_numbers) multiples_of_three = (x for x in natural_numbers if x % 3 == 0)

Be aware that an infinite generator does not have an end, so passing it to any function that will attempt to consume the generator entirely will have dire consequences: list(multiples_of_two)

# will never terminate, or raise an OS-specific error

Instead, use list/set comprehensions with range (or xrange for python < 3.0): first_five_multiples_of_three = [next(multiples_of_three) for _ in range(5)] # [3, 6, 9, 12, 15]

or use itertools.islice() to slice the iterator to a subset: from itertools import islice multiples_of_four = (x * 4 for x in integers_starting_from(1)) first_five_multiples_of_four = list(islice(multiples_of_four, 5)) # [4, 8, 12, 16, 20]

Note that the original generator is updated too, just like all other generators coming from the same "root": next(natural_numbers) next(multiples_of_two) next(multiples_of_four)

# yields 16 # yields 34 # yields 24

An infinite sequence can also be iterated with a for-loop. Make sure to include a conditional break statement so that the loop would terminate eventually: for idx, number in enumerate(multiplies_of_two): print(number) if idx == 9:

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break

# stop after taking the first 10 multiplies of two

Classic example - Fibonacci numbers import itertools def fibonacci(): a, b = 1, 1 while True: yield a a, b = b, a + b first_ten_fibs = list(itertools.islice(fibonacci(), 10)) # [1, 1, 2, 3, 5, 8, 13, 21, 34, 55] def nth_fib(n): return next(itertools.islice(fibonacci(), n - 1, n)) ninety_nineth_fib = nth_fib(99)

# 354224848179261915075

Section 67.3: Sending objects to a generator In addition to receiving values from a generator, it is possible to send an object to a generator using the send() method. def accumulator(): total = 0 value = None while True: # receive sent value value = yield total if value is None: break # aggregate values total += value generator = accumulator() # advance until the first "yield" next(generator) # 0 # from this point on, the generator aggregates values generator.send(1) # 1 generator.send(10) # 11 generator.send(100) # 111 # ... # Calling next(generator) is equivalent to calling generator.send(None) next(generator) # StopIteration

What happens here is the following: When you first call next(generator), the program advances to the first yield statement, and returns the value of total at that point, which is 0. The execution of the generator suspends at this point. When you then call generator.send(x), the interpreter takes the argument x and makes it the return value of the last yield statement, which gets assigned to value. The generator then proceeds as usual, until it yields the next value. When you finally call next(generator), the program treats this as if you're sending None to the generator. There is nothing special about None, however, this example uses None as a special value to ask the generator to stop. GoalKicker.com – Python® Notes for Professionals

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Section 67.4: Yielding all values from another iterable Python 3.x Version

≥ 3.3

Use yield from if you want to yield all values from another iterable: def foob(x): yield from range(x * 2) yield from range(2) list(foob(5))

# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1]

This works with generators as well. def fibto(n): a, b = 1, 1 while True: if a >= n: break yield a a, b = b, a + b def usefib(): yield from fibto(10) yield from fibto(20) list(usefib())

# [1, 1, 2, 3, 5, 8, 1, 1, 2, 3, 5, 8, 13]

Section 67.5: Iteration A generator object supports the iterator protocol. That is, it provides a next() method (__next__() in Python 3.x), which is used to step through its execution, and its __iter__ method returns itself. This means that a generator can be used in any language construct which supports generic iterable objects. # naive partial implementation of the Python 2.x xrange() def xrange(n): i = 0 while i < n: yield i i += 1 # looping for i in xrange(10): print(i) # prints the values 0, 1, ..., 9 # unpacking a, b, c = xrange(3) # building a list l = list(xrange(10))

# 0, 1, 2

# [0, 1, ..., 9]

Section 67.6: The next() function The next() built-in is a convenient wrapper which can be used to receive a value from any iterator (including a generator iterator) and to provide a default value in case the iterator is exhausted. def nums(): yield 1

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yield 2 yield 3 generator = nums() next(generator, next(generator, next(generator, next(generator, next(generator, # ...

None) None) None) None) None)

# # # # #

1 2 3 None None

The syntax is next(iterator[, default]). If iterator ends and a default value was passed, it is returned. If no default was provided, StopIteration is raised.

Section 67.7: Coroutines Generators can be used to implement coroutines: # create and advance generator to the first yield def coroutine(func): def start(*args,**kwargs): cr = func(*args,**kwargs) next(cr) return cr return start # example coroutine @coroutine def adder(sum = 0): while True: x = yield sum sum += x # example use s = adder() s.send(1) # 1 s.send(2) # 3

Coroutines are commonly used to implement state machines, as they are primarily useful for creating singlemethod procedures that require a state to function properly. They operate on an existing state and return the value obtained on completion of the operation.

Section 67.8: Refactoring list-building code Suppose you have complex code that creates and returns a list by starting with a blank list and repeatedly appending to it: def create(): result = [] # logic here... result.append(value) # possibly in several places # more logic... return result # possibly in several places values = create()

When it's not practical to replace the inner logic with a list comprehension, you can turn the entire function into a generator in-place, and then collect the results: GoalKicker.com – Python® Notes for Professionals

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def create_gen(): # logic... yield value # more logic return # not needed if at the end of the function, of course values = list(create_gen())

If the logic is recursive, use yield from to include all the values from the recursive call in a "flattened" result: def preorder_traversal(node): yield node.value for child in node.children: yield from preorder_traversal(child)

Section 67.9: Yield with recursion: recursively listing all files in a directory First, import the libraries that work with files: from os import listdir from os.path import isfile, join, exists

A helper function to read only files from a directory: def get_files(path): for file in listdir(path): full_path = join(path, file) if isfile(full_path): if exists(full_path): yield full_path

Another helper function to get only the subdirectories: def get_directories(path): for directory in listdir(path): full_path = join(path, directory) if not isfile(full_path): if exists(full_path): yield full_path

Now use these functions to recursively get all files within a directory and all its subdirectories (using generators): def get_files_recursive(directory): for file in get_files(directory): yield file for subdirectory in get_directories(directory): for file in get_files_recursive(subdirectory): # here the recursive call yield file

This function can be simplified using yield from: def get_files_recursive(directory): yield from get_files(directory) for subdirectory in get_directories(directory): yield from get_files_recursive(subdirectory)

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Section 67.10: Generator expressions It's possible to create generator iterators using a comprehension-like syntax. generator = (i * 2 for i in range(3)) next(generator) next(generator) next(generator) next(generator)

# # # #

0 2 4 raises StopIteration

If a function doesn't necessarily need to be passed a list, you can save on characters (and improve readability) by placing a generator expression inside a function call. The parenthesis from the function call implicitly make your expression a generator expression. sum(i ** 2 for i in range(4))

# 0^2 + 1^2 + 2^2 + 3^2 = 0 + 1 + 4 + 9 = 14

Additionally, you will save on memory because instead of loading the entire list you are iterating over ([0, 1, 2, 3] in the above example), the generator allows Python to use values as needed.

Section 67.11: Using a generator to find Fibonacci Numbers A practical use case of a generator is to iterate through values of an infinite series. Here's an example of finding the first ten terms of the Fibonacci Sequence. def fib(a=0, b=1): """Generator that yields Fibonacci numbers. `a` and `b` are the seed values""" while True: yield a a, b = b, a + b f = fib() print(', '.join(str(next(f)) for _ in range(10)))

0, 1, 1, 2, 3, 5, 8, 13, 21, 34

Section 67.12: Searching The next function is useful even without iterating. Passing a generator expression to next is a quick way to search for the first occurrence of an element matching some predicate. Procedural code like def find_and_transform(sequence, predicate, func): for element in sequence: if predicate(element): return func(element) raise ValueError item = find_and_transform(my_sequence, my_predicate, my_func)

can be replaced with: item = next(my_func(x) for x in my_sequence if my_predicate(x)) # StopIteration will be raised if there are no matches; this exception can # be caught and transformed, if desired.

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For this purpose, it may be desirable to create an alias, such as first = next, or a wrapper function to convert the exception: def first(generator): try: return next(generator) except StopIteration: raise ValueError

Section 67.13: Iterating over generators in parallel To iterate over several generators in parallel, use the zip builtin: for x, y in zip(a,b): print(x,y)

Results in: 1 x 2 y 3 z

In python 2 you should use itertools.izip instead. Here we can also see that the all the zip functions yield tuples. Note that zip will stop iterating as soon as one of the iterables runs out of items. If you'd like to iterate for as long as the longest iterable, use itertools.zip_longest().

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Chapter 68: Reduce Parameter Details function function that is used for reducing the iterable (must take two arguments). (positional-only) iterable

iterable that's going to be reduced. (positional-only)

initializer

start-value of the reduction. (optional, positional-only)

Section 68.1: Overview # No import needed

# No import required... from functools import reduce # ... but it can be loaded from the functools module

from functools import reduce # mandatory reduce reduces an iterable by applying a function repeatedly on the next element of an iterable and the

cumulative result so far. def add(s1, s2): return s1 + s2 asequence = [1, 2, 3] reduce(add, asequence) # Out: 6

# equivalent to: add(add(1,2),3)

In this example, we defined our own add function. However, Python comes with a standard equivalent function in the operator module: import operator reduce(operator.add, asequence) # Out: 6 reduce can also be passed a starting value: reduce(add, asequence, 10) # Out: 16

Section 68.2: Using reduce def multiply(s1, s2): print('{arg1} * {arg2} = {res}'.format(arg1=s1, arg2=s2, res=s1*s2)) return s1 * s2 asequence = [1, 2, 3]

Given an initializer the function is started by applying it to the initializer and the first iterable element: cumprod = reduce(multiply, asequence, 5) # Out: 5 * 1 = 5

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# 5 * 2 = 10 # 10 * 3 = 30 print(cumprod) # Out: 30

Without initializer parameter the reduce starts by applying the function to the first two list elements: cumprod = reduce(multiply, asequence) # Out: 1 * 2 = 2 # 2 * 3 = 6 print(cumprod) # Out: 6

Section 68.3: Cumulative product import operator reduce(operator.mul, [10, 5, -3]) # Out: -150

Section 68.4: Non short-circuit variant of any/all reduce will not terminate the iteration before the iterable has been completely iterated over so it can be used to

create a non short-circuit any() or all() function: import operator # non short-circuit "all" reduce(operator.and_, [False, True, True, True]) # = False # non short-circuit "any" reduce(operator.or_, [True, False, False, False]) # = True

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Chapter 69: Map Function Parameter function

Details function for mapping (must take as many parameters as there are iterables) (positional-only)

iterable

the function is applied to each element of the iterable (positional-only)

*additional_iterables see iterable, but as many as you like (optional, positional-only)

Section 69.1: Basic use of map, itertools.imap and future_builtins.map The map function is the simplest one among Python built-ins used for functional programming. map() applies a specified function to each element in an iterable: names = ['Fred', 'Wilma', 'Barney']

Python 3.x Version

≥ 3.0

map(len, names) # map in Python 3.x is a class; its instances are iterable # Out:

A Python 3-compatible map is included in the future_builtins module: Python 2.x Version

≥ 2.6

from future_builtins import map # contains a Python 3.x compatible map() map(len, names) # see below # Out:

Alternatively, in Python 2 one can use imap from itertools to get a generator Python 2.x Version map(len, names) # Out: [4, 5, 6]

≥ 2.3

# map() returns a list

from itertools import imap imap(len, names) # itertools.imap() returns a generator # Out:

The result can be explicitly converted to a list to remove the differences between Python 2 and 3: list(map(len, names)) # Out: [4, 5, 6] map() can be replaced by an equivalent list comprehension or generator expression: [len(item) for item in names] # equivalent to Python 2.x map() # Out: [4, 5, 6] (len(item) for item in names) # equivalent to Python 3.x map() # Out:

Section 69.2: Mapping each value in an iterable For example, you can take the absolute value of each element: list(map(abs, (1, -1, 2, -2, 3, -3))) # the call to `list` is unnecessary in 2.x

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# Out: [1, 1, 2, 2, 3, 3]

Anonymous function also support for mapping a list: map(lambda x:x*2, [1, 2, 3, 4, 5]) # Out: [2, 4, 6, 8, 10]

or converting decimal values to percentages: def to_percent(num): return num * 100 list(map(to_percent, [0.95, 0.75, 1.01, 0.1])) # Out: [95.0, 75.0, 101.0, 10.0]

or converting dollars to euros (given an exchange rate): from functools import partial from operator import mul rate = 0.9 # fictitious exchange rate, 1 dollar = 0.9 euros dollars = {'under_my_bed': 1000, 'jeans': 45, 'bank': 5000} sum(map(partial(mul, rate), dollars.values())) # Out: 5440.5 functools.partial is a convenient way to fix parameters of functions so that they can be used with map instead of

using lambda or creating customized functions.

Section 69.3: Mapping values of dierent iterables For example calculating the average of each i-th element of multiple iterables: def average(*args): return float(sum(args)) / len(args)

# cast to float - only mandatory for python 2.x

measurement1 = [100, 111, 99, 97] measurement2 = [102, 117, 91, 102] measurement3 = [104, 102, 95, 101] list(map(average, measurement1, measurement2, measurement3)) # Out: [102.0, 110.0, 95.0, 100.0]

There are different requirements if more than one iterable is passed to map depending on the version of python: The function must take as many parameters as there are iterables: def median_of_three(a, b, c): return sorted((a, b, c))[1] list(map(median_of_three, measurement1, measurement2))

TypeError: median_of_three() missing 1 required positional argument: 'c'

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list(map(median_of_three, measurement1, measurement2, measurement3, measurement3))

TypeError: median_of_three() takes 3 positional arguments but 4 were given

Python 2.x Version

≥ 2.0.1

map: The mapping iterates as long as one iterable is still not fully consumed but assumes None from the fully

consumed iterables: import operator measurement1 = [100, 111, 99, 97] measurement2 = [102, 117] # Calculate difference between elements list(map(operator.sub, measurement1, measurement2))

TypeError: unsupported operand type(s) for -: 'int' and 'NoneType'

itertools.imap and future_builtins.map: The mapping stops as soon as one iterable stops: import operator from itertools import imap measurement1 = [100, 111, 99, 97] measurement2 = [102, 117] # Calculate difference between elements list(imap(operator.sub, measurement1, measurement2)) # Out: [-2, -6] list(imap(operator.sub, measurement2, measurement1)) # Out: [2, 6]

Python 3.x Version

≥ 3.0.0

The mapping stops as soon as one iterable stops: import operator measurement1 = [100, 111, 99, 97] measurement2 = [102, 117] # Calculate difference between elements list(map(operator.sub, measurement1, measurement2)) # Out: [-2, -6] list(map(operator.sub, measurement2, measurement1)) # Out: [2, 6]

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Section 69.4: Transposing with Map: Using "None" as function argument (python 2.x only) from itertools import imap from future_builtins import map as fmap # Different name to highlight differences image = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] list(map(None, *image)) # Out: [(1, 4, 7), (2, 5, 8), (3, 6, 9)] list(fmap(None, *image)) # Out: [(1, 4, 7), (2, 5, 8), (3, 6, 9)] list(imap(None, *image)) # Out: [(1, 4, 7), (2, 5, 8), (3, 6, 9)] image2 = [[1, 2, 3], [4, 5], [7, 8, 9]] list(map(None, *image2)) # Out: [(1, 4, 7), (2, 5, 8), (3, None, 9)] list(fmap(None, *image2)) # Out: [(1, 4, 7), (2, 5, 8)] list(imap(None, *image2)) # Out: [(1, 4, 7), (2, 5, 8)]

Python 3.x Version

# Fill missing values with None # ignore columns with missing values # dito

≥ 3.0.0

list(map(None, *image))

TypeError: 'NoneType' object is not callable But there is a workaround to have similar results: def conv_to_list(*args): return list(args) list(map(conv_to_list, *image)) # Out: [[1, 4, 7], [2, 5, 8], [3, 6, 9]]

Section 69.5: Series and Parallel Mapping map() is a built-in function, which means that it is available everywhere without the need to use an 'import' statement. It is available everywhere just like print() If you look at Example 5 you will see that I had to use an import statement before I could use pretty print (import pprint). Thus pprint is not a built-in function Series mapping In this case each argument of the iterable is supplied as argument to the mapping function in ascending order. This arises when we have just one iterable to map and the mapping function requires a single argument. Example 1 insects = ['fly', 'ant', 'beetle', 'cankerworm'] f = lambda x: x + ' is an insect' print(list(map(f, insects))) # the function defined by f is executed on each item of the iterable

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insects

results in ['fly is an insect', 'ant is an insect', 'beetle is an insect', 'cankerworm is an insect']

Example 2 print(list(map(len, insects))) # the len function is executed each item in the insect list

results in [3, 3, 6, 10]

Parallel mapping In this case each argument of the mapping function is pulled from across all iterables (one from each iterable) in parallel. Thus the number of iterables supplied must match the number of arguments required by the function. carnivores = ['lion', 'tiger', 'leopard', 'arctic fox'] herbivores = ['african buffalo', 'moose', 'okapi', 'parakeet'] omnivores = ['chicken', 'dove', 'mouse', 'pig'] def animals(w, x, y, z): return '{0}, {1}, {2}, and {3} ARE ALL ANIMALS'.format(w.title(), x, y, z)

Example 3 # Too many arguments # observe here that map is trying to pass one item each from each of the four iterables to len. This leads len to complain that # it is being fed too many arguments print(list(map(len, insects, carnivores, herbivores, omnivores)))

results in TypeError: len() takes exactly one argument (4 given)

Example 4 # Too few arguments # observe here that map is supposed to execute animal on individual elements of insects one-by-one. But animals complain when # it only gets one argument, whereas it was expecting four. print(list(map(animals, insects)))

results in TypeError: animals() missing 3 required positional arguments: 'x', 'y', and 'z'

Example 5 # here map supplies w, x, y, z with one value from across the list import pprint pprint.pprint(list(map(animals, insects, carnivores, herbivores, omnivores)))

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results in ['Fly, lion, african buffalo, and chicken ARE ALL ANIMALS', 'Ant, tiger, moose, and dove ARE ALL ANIMALS', 'Beetle, leopard, okapi, and mouse ARE ALL ANIMALS', 'Cankerworm, arctic fox, parakeet, and pig ARE ALL ANIMALS']

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Chapter 70: Exponentiation Section 70.1: Exponentiation using builtins: ** and pow() Exponentiation can be used by using the builtin pow-function or the ** operator: 2 ** 3 # 8 pow(2, 3) # 8

For most (all in Python 2.x) arithmetic operations the result's type will be that of the wider operand. This is not true for **; the following cases are exceptions from this rule: Base: int, exponent: int < 0: 2 ** -3 # Out: 0.125 (result is a float)

This is also valid for Python 3.x. Before Python 2.2.0, this raised a ValueError. Base: int < 0 or float < 0, exponent: float != int (-2) ** (0.5) # also (-2.) ** (0.5) # Out: (8.659560562354934e-17+1.4142135623730951j) (result is complex)

Before python 3.0.0, this raised a ValueError. The operator module contains two functions that are equivalent to the **-operator: import operator operator.pow(4, 2) operator.__pow__(4, 3)

# 16 # 64

or one could directly call the __pow__ method: val1, val2 = 4, 2 val1.__pow__(val2) # 16 val2.__rpow__(val1) # 16 # in-place power operation isn't supported by immutable classes like int, float, complex: # val1.__ipow__(val2)

Section 70.2: Square root: math.sqrt() and cmath.sqrt The math module contains the math.sqrt()-function that can compute the square root of any number (that can be converted to a float) and the result will always be a float: import math math.sqrt(9) math.sqrt(11.11) math.sqrt(Decimal('6.25'))

# 3.0 # 3.3331666624997918 # 2.5

The math.sqrt() function raises a ValueError if the result would be complex: GoalKicker.com – Python® Notes for Professionals

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math.sqrt(-10)

ValueError: math domain error math.sqrt(x) is faster than math.pow(x, 0.5) or x ** 0.5 but the precision of the results is the same. The cmath

module is extremely similar to the math module, except for the fact it can compute complex numbers and all of its results are in the form of a + bi. It can also use .sqrt(): import cmath cmath.sqrt(4) # 2+0j cmath.sqrt(-4) # 2j

What's with the j? j is the equivalent to the square root of -1. All numbers can be put into the form a + bi, or in this case, a + bj. a is the real part of the number like the 2 in 2+0j. Since it has no imaginary part, b is 0. b represents part of the imaginary part of the number like the 2 in 2j. Since there is no real part in this, 2j can also be written as 0 + 2j.

Section 70.3: Modular exponentiation: pow() with 3 arguments Supplying pow() with 3 arguments pow(a, b, c) evaluates the modular exponentiation ab mod c: pow(3, 4, 17)

# 13

# equivalent unoptimized expression: 3 ** 4 % 17 # 13 # steps: 3 ** 4 81 % 17

# 81 # 13

For built-in types using modular exponentiation is only possible if: First argument is an int Second argument is an int >= 0 Third argument is an int != 0 These restrictions are also present in python 3.x For example one can use the 3-argument form of pow to define a modular inverse function: def modular_inverse(x, p): """Find a such as a·x ≡ 1 (mod p), assuming p is prime.""" return pow(x, p-2, p) [modular_inverse(x, 13) for x in range(1,13)] # Out: [1, 7, 9, 10, 8, 11, 2, 5, 3, 4, 6, 12]

Section 70.4: Computing large integer roots Even though Python natively supports big integers, taking the nth root of very large numbers can fail in Python. x = 2 ** 100 cube = x ** 3

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root = cube ** (1.0 / 3)

OverflowError: long int too large to convert to float When dealing with such large integers, you will need to use a custom function to compute the nth root of a number. def nth_root(x, n): # Start with some reasonable bounds around the nth root. upper_bound = 1 while upper_bound ** n mid and mid_nth > x: upper_bound = mid else: # Found perfect nth root. return mid return mid + 1 x = 2 ** 100 cube = x ** 3 root = nth_root(cube, 3) x == root # True

Section 70.5: Exponentiation using the math module: math.pow() The math-module contains another math.pow() function. The difference to the builtin pow()-function or ** operator is that the result is always a float: import math math.pow(2, 2) math.pow(-2., 2)

# 4.0 # 4.0

Which excludes computations with complex inputs: math.pow(2, 2+0j)

TypeError: can't convert complex to float and computations that would lead to complex results: math.pow(-2, 0.5)

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Section 70.6: Exponential function: math.exp() and cmath.exp() Both the math and cmath-module contain the Euler number: e and using it with the builtin pow()-function or **operator works mostly like math.exp(): import math math.e ** 2 math.exp(2)

# 7.3890560989306495 # 7.38905609893065

import cmath cmath.e ** 2 # 7.3890560989306495 cmath.exp(2) # (7.38905609893065+0j)

However the result is different and using the exponential function directly is more reliable than builtin exponentiation with base math.e: print(math.e ** 10) # print(math.exp(10)) # print(cmath.exp(10).real) # # difference starts here

22026.465794806703 22026.465794806718 22026.465794806718 ---------------^

Section 70.7: Exponential function minus 1: math.expm1() The math module contains the expm1()-function that can compute the expression math.e ** x - 1 for very small x with higher precision than math.exp(x) or cmath.exp(x) would allow: import math print(math.e ** 1e-3 - 1) print(math.exp(1e-3) - 1) print(math.expm1(1e-3)) #

# 0.0010005001667083846 # 0.0010005001667083846 # 0.0010005001667083417 ------------------^

For very small x the difference gets bigger: print(math.e ** 1e-15 - 1) # 1.1102230246251565e-15 print(math.exp(1e-15) - 1) # 1.1102230246251565e-15 print(math.expm1(1e-15)) # 1.0000000000000007e-15 # ^-------------------

The improvement is significant in scientific computing. For example the Planck's law contains an exponential function minus 1: def planks_law(lambda_, T): from scipy.constants import h, k, c # If no scipy installed hardcode these! return 2 * h * c ** 2 / (lambda_ ** 5 * math.expm1(h * c / (lambda_ * k * T))) def planks_law_naive(lambda_, T): from scipy.constants import h, k, c # If no scipy installed hardcode these! return 2 * h * c ** 2 / (lambda_ ** 5 * (math.e ** (h * c / (lambda_ * k * T)) - 1)) planks_law(100, 5000) planks_law_naive(100, 5000) #

# 4.139080074896474e-19 # 4.139080073488451e-19 ^----------

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planks_law(1000, 5000) # 4.139080128493406e-23 planks_law_naive(1000, 5000) # 4.139080233183142e-23 # ^------------

Section 70.8: Magic methods and exponentiation: builtin, math and cmath Supposing you have a class that stores purely integer values: class Integer(object): def __init__(self, value): self.value = int(value) # Cast to an integer def __repr__(self): return '{cls}({val})'.format(cls=self.__class__.__name__, val=self.value) def __pow__(self, other, modulo=None): if modulo is None: print('Using __pow__') return self.__class__(self.value ** other) else: print('Using __pow__ with modulo') return self.__class__(pow(self.value, other, modulo)) def __float__(self): print('Using __float__') return float(self.value) def __complex__(self): print('Using __complex__') return complex(self.value, 0)

Using the builtin pow function or ** operator always calls __pow__: Integer(2) ** 2 # Prints: Using __pow__ Integer(2) ** 2.5 # Prints: Using __pow__ pow(Integer(2), 0.5) # Prints: Using __pow__ operator.pow(Integer(2), 3) # Prints: Using __pow__ operator.__pow__(Integer(3), 3) # Prints: Using __pow__

# Integer(4) # Integer(5) # Integer(1) # Integer(8) # Integer(27)

The second argument of the __pow__() method can only be supplied by using the builtin-pow() or by directly calling the method: pow(Integer(2), 3, 4) # Integer(0) # Prints: Using __pow__ with modulo Integer(2).__pow__(3, 4) # Integer(0) # Prints: Using __pow__ with modulo

While the math-functions always convert it to a float and use the float-computation: import math

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math.pow(Integer(2), 0.5) # 1.4142135623730951 # Prints: Using __float__ cmath-functions try to convert it to complex but can also fallback to float if there is no explicit conversion to complex: import cmath cmath.exp(Integer(2)) # (7.38905609893065+0j) # Prints: Using __complex__ del Integer.__complex__

# Deleting __complex__ method - instances cannot be cast to complex

cmath.exp(Integer(2)) # (7.38905609893065+0j) # Prints: Using __float__

Neither math nor cmath will work if also the __float__()-method is missing: del Integer.__float__

# Deleting __complex__ method

math.sqrt(Integer(2))

# also cmath.exp(Integer(2))

TypeError: a float is required

Section 70.9: Roots: nth-root with fractional exponents While the math.sqrt function is provided for the specific case of square roots, it's often convenient to use the exponentiation operator (**) with fractional exponents to perform nth-root operations, like cube roots. The inverse of an exponentiation is exponentiation by the exponent's reciprocal. So, if you can cube a number by putting it to the exponent of 3, you can find the cube root of a number by putting it to the exponent of 1/3. >>> x >>> y >>> y 27 >>> z >>> z 3.0 >>> z True

= 3 = x ** 3

= y ** (1.0 / 3)

== x

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Chapter 71: Searching Section 71.1: Searching for an element All built-in collections in Python implement a way to check element membership using in. List alist = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] 5 in alist # True 10 in alist # False

Tuple atuple = ('0', '1', '2', '3', '4') 4 in atuple # False '4' in atuple # True

String astring = 'i am a string' 'a' in astring # True 'am' in astring # True 'I' in astring # False

Set aset = {(10, 10), (20, 20), (30, 30)} (10, 10) in aset # True 10 in aset # False

Dict dict is a bit special: the normal in only checks the keys. If you want to search in values you need to specify it. The

same if you want to search for key-value pairs. adict = {0: 'a', 1: 'b', 2: 'c', 3: 1 in adict # True 'a' in adict # False 2 in adict.keys() # True 'a' in adict.values() # True (0, 'a') in adict.items() # True

'd'} - implicitly searches in keys - explicitly searches in keys - explicitly searches in values - explicitly searches key/value pairs

Section 71.2: Searching in custom classes: __contains__ and __iter__ To allow the use of in for custom classes the class must either provide the magic method __contains__ or, failing that, an __iter__-method. Suppose you have a class containing a list of lists: class ListList: def __init__(self, value): self.value = value # Create a set of all values for fast access self.setofvalues = set(item for sublist in self.value for item in sublist) def __iter__(self): print('Using __iter__.') # A generator over all sublist elements return (item for sublist in self.value for item in sublist)

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def __contains__(self, value): print('Using __contains__.') # Just lookup if the value is in the set return value in self.setofvalues # Even without the set you could use the iter method for the contains-check: # return any(item == value for item in iter(self))

Using membership testing is possible using in: a = ListList([[1,1,1],[0,1,1],[1,5,1]]) 10 in a # False # Prints: Using __contains__. 5 in a # True # Prints: Using __contains__.

even after deleting the __contains__ method: del ListList.__contains__ 5 in a # True # Prints: Using __iter__.

Note: The looping in (as in for i in a) will always use __iter__ even if the class implements a __contains__ method.

Section 71.3: Getting the index for strings: str.index(), str.rindex() and str.find(), str.rfind() String also have an index method but also more advanced options and the additional str.find. For both of these

there is a complementary reversed method. astring = 'Hello on StackOverflow' astring.index('o') # 4 astring.rindex('o') # 20 astring.find('o') astring.rfind('o')

# 4 # 20

The difference between index/rindex and find/rfind is what happens if the substring is not found in the string: astring.index('q') # ValueError: substring not found astring.find('q') # -1

All of these methods allow a start and end index: astring.index('o', astring.index('o', astring.index('o', astring.index('o',

5) # 6 6) # 6 - start is inclusive 5, 7) # 6 5, 6) # - end is not inclusive

ValueError: substring not found

astring.rindex('o', 20) # 20 astring.rindex('o', 19) # 20 - still from left to right

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astring.rindex('o', 4, 7) # 6

Section 71.4: Getting the index list and tuples: list.index(), tuple.index() list and tuple have an index-method to get the position of the element: alist = [10, 16, 26, 5, 2, 19, 105, 26] # search for 16 in the list alist.index(16) # 1 alist[1] # 16 alist.index(15)

ValueError: 15 is not in list But only returns the position of the first found element: atuple = (10, 16, 26, 5, 2, 19, 105, 26) atuple.index(26) # 2 atuple[2] # 26 atuple[7] # 26 - is also 26!

Section 71.5: Searching key(s) for a value in dict dict have no builtin method for searching a value or key because dictionaries are unordered. You can create a

function that gets the key (or keys) for a specified value: def getKeysForValue(dictionary, value): foundkeys = [] for keys in dictionary: if dictionary[key] == value: foundkeys.append(key) return foundkeys

This could also be written as an equivalent list comprehension: def getKeysForValueComp(dictionary, value): return [key for key in dictionary if dictionary[key] == value]

If you only care about one found key: def getOneKeyForValue(dictionary, value): return next(key for key in dictionary if dictionary[key] == value)

The first two functions will return a list of all keys that have the specified value: adict = {'a': 10, 'b': 20, getKeysForValue(adict, 10) getKeysForValueComp(adict, getKeysForValueComp(adict, getKeysForValueComp(adict,

'c': 10} # ['c', 'a'] - order is random could as well be ['a', 'c'] 10) # ['c', 'a'] - dito 20) # ['b'] 25) # []

The other one will only return one key: GoalKicker.com – Python® Notes for Professionals

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getOneKeyForValue(adict, 10) getOneKeyForValue(adict, 20)

# 'c' # 'b'

- depending on the circumstances this could also be 'a'

and raise a StopIteration-Exception if the value is not in the dict: getOneKeyForValue(adict, 25)

StopIteration

Section 71.6: Getting the index for sorted sequences: bisect.bisect_left() Sorted sequences allow the use of faster searching algorithms: bisect.bisect_left()1: import bisect def index_sorted(sorted_seq, value): """Locate the leftmost value exactly equal to x or raise a ValueError""" i = bisect.bisect_left(sorted_seq, value) if i != len(sorted_seq) and sorted_seq[i] == value: return i raise ValueError alist = [i for i in index_sorted(alist, index_sorted(alist, index_sorted(alist,

range(1, 100000, 3)] # Sorted list from 1 to 100000 with step 3 97285) # 32428 4) # 1 97286)

ValueError For very large sorted sequences the speed gain can be quite high. In case for the first search approximately 500 times as fast: %timeit index_sorted(alist, 97285) # 100000 loops, best of 3: 3 µs per loop %timeit alist.index(97285) # 1000 loops, best of 3: 1.58 ms per loop

While it's a bit slower if the element is one of the very first: %timeit index_sorted(alist, 4) # 100000 loops, best of 3: 2.98 µs per loop %timeit alist.index(4) # 1000000 loops, best of 3: 580 ns per loop

Section 71.7: Searching nested sequences Searching in nested sequences like a list of tuple requires an approach like searching the keys for values in dict but needs customized functions. The index of the outermost sequence if the value was found in the sequence:

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def outer_index(nested_sequence, value): return next(index for index, inner in enumerate(nested_sequence) for item in inner if item == value) alist_of_tuples = [(4, 5, 6), (3, 1, 'a'), (7, 0, 4.3)] outer_index(alist_of_tuples, 'a') # 1 outer_index(alist_of_tuples, 4.3) # 2

or the index of the outer and inner sequence: def outer_inner_index(nested_sequence, value): return next((oindex, iindex) for oindex, inner in enumerate(nested_sequence) for iindex, item in enumerate(inner) if item == value) outer_inner_index(alist_of_tuples, 'a') # (1, 2) alist_of_tuples[1][2] # 'a' outer_inner_index(alist_of_tuples, 7) alist_of_tuples[2][0] # 7

# (2, 0)

In general (not always) using next and a generator expression with conditions to find the first occurrence of the searched value is the most efficient approach.

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Chapter 72: Sorting, Minimum and Maximum Section 72.1: Make custom classes orderable min, max, and sorted all need the objects to be orderable. To be properly orderable, the class needs to define all of

the 6 methods __lt__, __gt__, __ge__, __le__, __ne__ and __eq__: class IntegerContainer(object): def __init__(self, value): self.value = value def __repr__(self): return "{}({})".format(self.__class__.__name__, self.value) def __lt__(self, other): print('{!r} - Test less than {!r}'.format(self, other)) return self.value < other.value def __le__(self, other): print('{!r} - Test less than or equal to {!r}'.format(self, other)) return self.value other.value def __ge__(self, other): print('{!r} - Test greater than or equal to {!r}'.format(self, other)) return self.value >= other.value def __eq__(self, other): print('{!r} - Test equal to {!r}'.format(self, other)) return self.value == other.value def __ne__(self, other): print('{!r} - Test not equal to {!r}'.format(self, other)) return self.value != other.value

Though implementing all these methods would seem unnecessary, omitting some of them will make your code prone to bugs. Examples: alist = [IntegerContainer(5), IntegerContainer(3), IntegerContainer(10), IntegerContainer(7) ] res = max(alist) # Out: IntegerContainer(3) - Test greater than IntegerContainer(5) # IntegerContainer(10) - Test greater than IntegerContainer(5) # IntegerContainer(7) - Test greater than IntegerContainer(10) print(res) # Out: IntegerContainer(10) res = min(alist) # Out: IntegerContainer(3) - Test less than IntegerContainer(5) # IntegerContainer(10) - Test less than IntegerContainer(3)

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# IntegerContainer(7) - Test less than IntegerContainer(3) print(res) # Out: IntegerContainer(3) res = sorted(alist) # Out: IntegerContainer(3) - Test less than IntegerContainer(5) # IntegerContainer(10) - Test less than IntegerContainer(3) # IntegerContainer(10) - Test less than IntegerContainer(5) # IntegerContainer(7) - Test less than IntegerContainer(5) # IntegerContainer(7) - Test less than IntegerContainer(10) print(res) # Out: [IntegerContainer(3), IntegerContainer(5), IntegerContainer(7), IntegerContainer(10)] sorted with reverse=True also uses __lt__: res = sorted(alist, reverse=True) # Out: IntegerContainer(10) - Test less than IntegerContainer(7) # IntegerContainer(3) - Test less than IntegerContainer(10) # IntegerContainer(3) - Test less than IntegerContainer(10) # IntegerContainer(3) - Test less than IntegerContainer(7) # IntegerContainer(5) - Test less than IntegerContainer(7) # IntegerContainer(5) - Test less than IntegerContainer(3) print(res) # Out: [IntegerContainer(10), IntegerContainer(7), IntegerContainer(5), IntegerContainer(3)]

But sorted can use __gt__ instead if the default is not implemented: del IntegerContainer.__lt__

# The IntegerContainer no longer implements "less than"

res = min(alist) # Out: IntegerContainer(5) - Test greater than IntegerContainer(3) # IntegerContainer(3) - Test greater than IntegerContainer(10) # IntegerContainer(3) - Test greater than IntegerContainer(7) print(res) # Out: IntegerContainer(3)

Sorting methods will raise a TypeError if neither __lt__ nor __gt__ are implemented: del IntegerContainer.__gt__

# The IntegerContainer no longer implements "greater then"

res = min(alist)

TypeError: unorderable types: IntegerContainer() < IntegerContainer() functools.total_ordering decorator can be used simplifying the effort of writing these rich comparison methods.

If you decorate your class with total_ordering, you need to implement __eq__, __ne__ and only one of the __lt__, __le__, __ge__ or __gt__, and the decorator will fill in the rest: import functools @functools.total_ordering class IntegerContainer(object): def __init__(self, value): self.value = value def __repr__(self): return "{}({})".format(self.__class__.__name__, self.value)

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def __lt__(self, other): print('{!r} - Test less than {!r}'.format(self, other)) return self.value < other.value def __eq__(self, other): print('{!r} - Test equal to {!r}'.format(self, other)) return self.value == other.value def __ne__(self, other): print('{!r} - Test not equal to {!r}'.format(self, other)) return self.value != other.value

IntegerContainer(5) > IntegerContainer(6) # Output: IntegerContainer(5) - Test less than IntegerContainer(6) # Returns: False IntegerContainer(6) > IntegerContainer(5) # Output: IntegerContainer(6) - Test less than IntegerContainer(5) # Output: IntegerContainer(6) - Test equal to IntegerContainer(5) # Returns True

Notice how the > (greater than) now ends up calling the less than method, and in some cases even the __eq__ method. This also means that if speed is of great importance, you should implement each rich comparison method yourself.

Section 72.2: Special case: dictionaries Getting the minimum or maximum or using sorted depends on iterations over the object. In the case of dict, the iteration is only over the keys: adict = {'a': 3, 'b': 5, 'c': 1} min(adict) # Output: 'a' max(adict) # Output: 'c' sorted(adict) # Output: ['a', 'b', 'c']

To keep the dictionary structure, you have to iterate over the .items(): min(adict.items()) # Output: ('a', 3) max(adict.items()) # Output: ('c', 1) sorted(adict.items()) # Output: [('a', 3), ('b', 5), ('c', 1)]

For sorted, you could create an OrderedDict to keep the sorting while having a dict-like structure: from collections import OrderedDict OrderedDict(sorted(adict.items())) # Output: OrderedDict([('a', 3), ('b', 5), ('c', 1)]) res = OrderedDict(sorted(adict.items())) res['a'] # Output: 3

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Again this is possible using the key argument: min(adict.items(), key=lambda x: x[1]) # Output: ('c', 1) max(adict.items(), key=operator.itemgetter(1)) # Output: ('b', 5) sorted(adict.items(), key=operator.itemgetter(1), reverse=True) # Output: [('b', 5), ('a', 3), ('c', 1)]

Section 72.3: Using the key argument Finding the minimum/maximum of a sequence of sequences is possible: list_of_tuples = [(0, 10), (1, 15), (2, 8)] min(list_of_tuples) # Output: (0, 10)

but if you want to sort by a specific element in each sequence use the key-argument: min(list_of_tuples, key=lambda x: x[0]) # Output: (0, 10)

# Sorting by first element

min(list_of_tuples, key=lambda x: x[1]) # Output: (2, 8)

# Sorting by second element

sorted(list_of_tuples, key=lambda x: x[0]) # Output: [(0, 10), (1, 15), (2, 8)]

# Sorting by first element (increasing)

sorted(list_of_tuples, key=lambda x: x[1]) # Output: [(2, 8), (0, 10), (1, 15)]

# Sorting by first element

import operator # The operator module contains efficient alternatives to the lambda function max(list_of_tuples, key=operator.itemgetter(0)) # Sorting by first element # Output: (2, 8) max(list_of_tuples, key=operator.itemgetter(1)) # Sorting by second element # Output: (1, 15) sorted(list_of_tuples, key=operator.itemgetter(0), reverse=True) # Reversed (decreasing) # Output: [(2, 8), (1, 15), (0, 10)] sorted(list_of_tuples, key=operator.itemgetter(1), reverse=True) # Reversed(decreasing) # Output: [(1, 15), (0, 10), (2, 8)]

Section 72.4: Default Argument to max, min You can't pass an empty sequence into max or min: min([])

ValueError: min() arg is an empty sequence However, with Python 3, you can pass in the keyword argument default with a value that will be returned if the sequence is empty, instead of raising an exception:

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max([], default=42) # Output: 42 max([], default=0) # Output: 0

Section 72.5: Getting a sorted sequence Using one sequence: sorted((7, 2, 1, 5)) # Output: [1, 2, 5, 7]

# tuple

sorted(['c', 'A', 'b']) # Output: ['A', 'b', 'c']

# list

sorted({11, 8, 1}) # Output: [1, 8, 11]

# set

sorted({'11': 5, '3': 2, '10': 15}) # dict # Output: ['10', '11', '3'] # only iterates over the keys sorted('bdca') # Output: ['a','b','c','d']

# string

The result is always a new list; the original data remains unchanged.

Section 72.6: Extracting N largest or N smallest items from an iterable To find some number (more than one) of largest or smallest values of an iterable, you can use the nlargest and nsmallest of the heapq module: import heapq # get 5 largest items from the range heapq.nlargest(5, range(10)) # Output: [9, 8, 7, 6, 5] heapq.nsmallest(5, range(10)) # Output: [0, 1, 2, 3, 4]

This is much more efficient than sorting the whole iterable and then slicing from the end or beginning. Internally these functions use the binary heap priority queue data structure, which is very efficient for this use case. Like min, max and sorted, these functions accept the optional key keyword argument, which must be a function that, given an element, returns its sort key. Here is a program that extracts 1000 longest lines from a file: import heapq with open(filename) as f: longest_lines = heapq.nlargest(1000, f, key=len)

Here we open the file, and pass the file handle f to nlargest. Iterating the file yields each line of the file as a separate string; nlargest then passes each element (or line) is passed to the function len to determine its sort key. GoalKicker.com – Python® Notes for Professionals

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len, given a string, returns the length of the line in characters.

This only needs storage for a list of 1000 largest lines so far, which can be contrasted with longest_lines = sorted(f, key=len)[1000:]

which will have to hold the entire file in memory.

Section 72.7: Getting the minimum or maximum of several values min(7,2,1,5) # Output: 1 max(7,2,1,5) # Output: 7

Section 72.8: Minimum and Maximum of a sequence Getting the minimum of a sequence (iterable) is equivalent of accessing the first element of a sorted sequence: min([2, 7, 5]) # Output: 2 sorted([2, 7, 5])[0] # Output: 2

The maximum is a bit more complicated, because sorted keeps order and max returns the first encountered value. In case there are no duplicates the maximum is the same as the last element of the sorted return: max([2, 7, 5]) # Output: 7 sorted([2, 7, 5])[-1] # Output: 7

But not if there are multiple elements that are evaluated as having the maximum value: class MyClass(object): def __init__(self, value, name): self.value = value self.name = name def __lt__(self, other): return self.value < other.value def __repr__(self): return str(self.name) sorted([MyClass(4, 'first'), MyClass(1, 'second'), MyClass(4, 'third')]) # Output: [second, first, third] max([MyClass(4, 'first'), MyClass(1, 'second'), MyClass(4, 'third')]) # Output: first

Any iterable containing elements that support < or > operations are allowed.

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Chapter 73: Counting Section 73.1: Counting all occurrence of all items in an iterable: collections.Counter from collections import Counter c = Counter(["a", "b", "c", "d", "a", "b", "a", "c", "d"]) c # Out: Counter({'a': 3, 'b': 2, 'c': 2, 'd': 2}) c["a"] # Out: 3 c[7] # not in the list (7 occurred 0 times!) # Out: 0

The collections.Counter can be used for any iterable and counts every occurrence for every element. Note: One exception is if a dict or another collections.Mapping-like class is given, then it will not count them, rather it creates a Counter with these values: Counter({"e": 2}) # Out: Counter({"e": 2}) Counter({"e": "e"}) # warning Counter does not verify the values are int # Out: Counter({"e": "e"})

Section 73.2: Getting the most common value(-s): collections.Counter.most_common() Counting the keys of a Mapping isn't possible with collections.Counter but we can count the values: from collections import Counter adict = {'a': 5, 'b': 3, 'c': 5, 'd': 2, 'e':2, 'q': 5} Counter(adict.values()) # Out: Counter({2: 2, 3: 1, 5: 3})

The most common elements are available by the most_common-method: # Sorting them from most-common to least-common value: Counter(adict.values()).most_common() # Out: [(5, 3), (2, 2), (3, 1)] # Getting the most common value Counter(adict.values()).most_common(1) # Out: [(5, 3)] # Getting the two most common values Counter(adict.values()).most_common(2) # Out: [(5, 3), (2, 2)]

Section 73.3: Counting the occurrences of one item in a sequence: list.count() and tuple.count() alist = [1, 2, 3, 4, 1, 2, 1, 3, 4]

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alist.count(1) # Out: 3 atuple = ('bear', 'weasel', 'bear', 'frog') atuple.count('bear') # Out: 2 atuple.count('fox') # Out: 0

Section 73.4: Counting the occurrences of a substring in a string: str.count() astring = 'thisisashorttext' astring.count('t') # Out: 4

This works even for substrings longer than one character: astring.count('th') # Out: 1 astring.count('is') # Out: 2 astring.count('text') # Out: 1

which would not be possible with collections.Counter which only counts single characters: from collections import Counter Counter(astring) # Out: Counter({'a': 1, 'e': 1, 'h': 2, 'i': 2, 'o': 1, 'r': 1, 's': 3, 't': 4, 'x': 1})

Section 73.5: Counting occurrences in numpy array To count the occurrences of a value in a numpy array. This will work: >>> import numpy as np >>> a=np.array([0,3,4,3,5,4,7]) >>> print np.sum(a==3) 2

The logic is that the boolean statement produces a array where all occurrences of the requested values are 1 and all others are zero. So summing these gives the number of occurencies. This works for arrays of any shape or dtype. There are two methods I use to count occurrences of all unique values in numpy. Unique and bincount. Unique automatically flattens multidimensional arrays, while bincount only works with 1d arrays only containing positive integers. >>> unique,counts=np.unique(a,return_counts=True) >>> print unique,counts # counts[i] is equal to occurrences of unique[i] in a [0 3 4 5 7] [1 2 2 1 1] >>> bin_count=np.bincount(a) >>> print bin_count # bin_count[i] is equal to occurrences of i in a [1 0 0 2 2 1 0 1]

If your data are numpy arrays it is generally much faster to use numpy methods then to convert your data to generic methods. GoalKicker.com – Python® Notes for Professionals

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Chapter 74: The Print Function Section 74.1: Print basics In Python 3 and higher, print is a function rather than a keyword. print('hello world!') # out: hello world! foo = 1 bar = 'bar' baz = 3.14 print(foo) # out: 1 print(bar) # out: bar print(baz) # out: 3.14

You can also pass a number of parameters to print: print(foo, bar, baz) # out: 1 bar 3.14

Another way to print multiple parameters is by using a + print(str(foo) + " " + bar + " " + str(baz)) # out: 1 bar 3.14

What you should be careful about when using + to print multiple parameters, though, is that the type of the parameters should be the same. Trying to print the above example without the cast to string first would result in an error, because it would try to add the number 1 to the string "bar" and add that to the number 3.14. # Wrong: # type:int str float print(foo + bar + baz) # will result in an error

This is because the content of print will be evaluated first: print(4 + 5) # out: 9 print("4" + "5") # out: 45 print([4] + [5]) # out: [4, 5]

Otherwise, using a + can be very helpful for a user to read output of variables In the example below the output is very easy to read! The script below demonstrates this import random #telling python to include a function to create random numbers randnum = random.randint(0, 12)

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#make a random number between 0 and 12 and assign it to a variable print("The randomly generated number was - " + str(randnum))

You can prevent the print function from automatically printing a newline by using the end parameter: print("this has no newline at the end of it... ", end="") print("see?") # out: this has no newline at the end of it... see?

If you want to write to a file, you can pass it as the parameter file: with open('my_file.txt', 'w+') as my_file: print("this goes to the file!", file=my_file)

this goes to the file!

Section 74.2: Print parameters You can do more than just print text. print also has several parameters to help you. Argument sep: place a string between arguments. Do you need to print a list of words separated by a comma or some other string? >>> print('apples','bananas', 'cherries', sep=', ') apple, bananas, cherries >>> print('apple','banana', 'cherries', sep=', ') apple, banana, cherries >>>

Argument end: use something other than a newline at the end Without the end argument, all print() functions write a line and then go to the beginning of the next line. You can change it to do nothing (use an empty string of ''), or double spacing between paragraphs by using two newlines. >>> print("")

>>> print("paragraph1", end="\n\n"); print("paragraph2") paragraph1 paragraph2 >>>

Argument file: send output to someplace other than sys.stdout. Now you can send your text to either stdout, a file, or StringIO and not care which you are given. If it quacks like a file, it works like a file. >>> def sendit(out, *values, sep=' ', end='\n'): ... print(*values, sep=sep, end=end, file=out) ... >>> sendit(sys.stdout, 'apples', 'bananas', 'cherries', sep='\t') apples bananas cherries >>> with open("delete-me.txt", "w+") as f: ... sendit(f, 'apples', 'bananas', 'cherries', sep=' ', end='\n')

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... >>> with open("delete-me.txt", "rt") as f: ... print(f.read()) ... apples bananas cherries >>>

There is a fourth parameter flush which will forcibly flush the stream.

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Chapter 75: Regular Expressions (Regex) Python makes regular expressions available through the re module. Regular expressions are combinations of characters that are interpreted as rules for matching substrings. For instance, the expression 'amount\D+\d+' will match any string composed by the word amount plus an integral number, separated by one or more non-digits, such as:amount=100, amount is 3, amount is equal to: 33, etc.

Section 75.1: Matching the beginning of a string The first argument of re.match() is the regular expression, the second is the string to match: import re pattern = r"123" string = "123zzb" re.match(pattern, string) # Out: match = re.match(pattern, string) match.group() # Out: '123'

You may notice that the pattern variable is a string prefixed with r, which indicates that the string is a raw string literal. A raw string literal has a slightly different syntax than a string literal, namely a backslash \ in a raw string literal means "just a backslash" and there's no need for doubling up backlashes to escape "escape sequences" such as newlines (\n), tabs (\t), backspaces (\), form-feeds (\r), and so on. In normal string literals, each backslash must be doubled up to avoid being taken as the start of an escape sequence. Hence, r"\n" is a string of 2 characters: \ and n. Regex patterns also use backslashes, e.g. \d refers to any digit character. We can avoid having to double escape our strings ("\\d") by using raw strings (r"\d"). For instance: string = "\\t123zzb" # here the backslash is escaped, so there's no tab, just '\' and 't' pattern = "\\t123" # this will match \t (escaping the backslash) followed by 123 re.match(pattern, string).group() # no match re.match(pattern, "\t123zzb").group() # matches '\t123' pattern = r"\\t123" re.match(pattern, string).group()

# matches '\\t123'

Matching is done from the start of the string only. If you want to match anywhere use re.search instead: match = re.match(r"(123)", "a123zzb") match is None # Out: True match = re.search(r"(123)", "a123zzb") match.group()

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# Out: '123'

Section 75.2: Searching pattern = r"(your base)" sentence = "All your base are belong to us." match = re.search(pattern, sentence) match.group(1) # Out: 'your base' match = re.search(r"(belong.*)", sentence) match.group(1) # Out: 'belong to us.'

Searching is done anywhere in the string unlike re.match. You can also use re.findall. You can also search at the beginning of the string (use ^), match = re.search(r"^123", "123zzb") match.group(0) # Out: '123' match = re.search(r"^123", "a123zzb") match is None # Out: True

at the end of the string (use $), match = re.search(r"123$", "zzb123") match.group(0) # Out: '123' match = re.search(r"123$", "123zzb") match is None # Out: True

or both (use both ^ and $): match = re.search(r"^123$", "123") match.group(0) # Out: '123'

Section 75.3: Precompiled patterns import re precompiled_pattern = re.compile(r"(\d+)") matches = precompiled_pattern.search("The answer is 41!") matches.group(1) # Out: 41 matches = precompiled_pattern.search("Or was it 42?") matches.group(1) # Out: 42

Compiling a pattern allows it to be reused later on in a program. However, note that Python caches recently-used GoalKicker.com – Python® Notes for Professionals

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expressions (docs, SO answer), so "programs that use only a few regular expressions at a time needn’t worry about compiling regular expressions". import re precompiled_pattern = re.compile(r"(.*\d+)") matches = precompiled_pattern.match("The answer is 41!") print(matches.group(1)) # Out: The answer is 41 matches = precompiled_pattern.match("Or was it 42?") print(matches.group(1)) # Out: Or was it 42

It can be used with re.match().

Section 75.4: Flags For some special cases we need to change the behavior of the Regular Expression, this is done using flags. Flags can be set in two ways, through the flags keyword or directly in the expression. Flags keyword Below an example for re.search but it works for most functions in the re module. m = re.search("b", "ABC") m is None # Out: True m = re.search("b", "ABC", flags=re.IGNORECASE) m.group() # Out: 'B' m = re.search("a.b", "A\nBC", flags=re.IGNORECASE) m is None # Out: True m = re.search("a.b", "A\nBC", flags=re.IGNORECASE|re.DOTALL) m.group() # Out: 'A\nB'

Common Flags Flag

Short Description

re.IGNORECASE, re.I Makes the pattern ignore the case re.DOTALL, re.S

Makes . match everything including newlines

re.MULTILINE, re.M Makes ^ match the begin of a line and $ the end of a line re.DEBUG

Turns on debug information

For the complete list of all available flags check the docs Inline flags From the docs: (?iLmsux) (One or more letters from the set 'i', 'L', 'm', 's', 'u', 'x'.)

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The group matches the empty string; the letters set the corresponding flags: re.I (ignore case), re.L (locale dependent), re.M (multi-line), re.S (dot matches all), re.U (Unicode dependent), and re.X (verbose), for the entire regular expression. This is useful if you wish to include the flags as part of the regular expression, instead of passing a flag argument to the re.compile() function. Note that the (?x) flag changes how the expression is parsed. It should be used first in the expression string, or after one or more whitespace characters. If there are non-whitespace characters before the flag, the results are undefined.

Section 75.5: Replacing Replacements can be made on strings using re.sub. Replacing strings re.sub(r"t[0-9][0-9]", "foo", "my name t13 is t44 what t99 ever t44") # Out: 'my name foo is foo what foo ever foo'

Using group references Replacements with a small number of groups can be made as follows: re.sub(r"t([0-9])([0-9])", r"t\2\1", "t13 t19 t81 t25") # Out: 't31 t91 t18 t52'

However, if you make a group ID like '10', this doesn't work: \10 is read as 'ID number 1 followed by 0'. So you have to be more specific and use the \g notation: re.sub(r"t([0-9])([0-9])", r"t\g\g", "t13 t19 t81 t25") # Out: 't31 t91 t18 t52'

Using a replacement function items = ["zero", "one", "two"] re.sub(r"a\[([0-3])\]", lambda match: items[int(match.group(1))], "Items: a[0], a[1], something, a[2]") # Out: 'Items: zero, one, something, two'

Section 75.6: Find All Non-Overlapping Matches re.findall(r"[0-9]{2,3}", "some 1 text 12 is 945 here 4445588899") # Out: ['12', '945', '444', '558', '889']

Note that the r before "[0-9]{2,3}" tells python to interpret the string as-is; as a "raw" string. You could also use re.finditer() which works in the same way as re.findall() but returns an iterator with SRE_Match objects instead of a list of strings: results = re.finditer(r"([0-9]{2,3})", "some 1 text 12 is 945 here 4445588899") print(results) # Out: for result in results: print(result.group(0)) ''' Out: 12 945 444 558

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889 '''

Section 75.7: Checking for allowed characters If you want to check that a string contains only a certain set of characters, in this case a-z, A-Z and 0-9, you can do so like this, import re def is_allowed(string): characherRegex = re.compile(r'[^a-zA-Z0-9.]') string = characherRegex.search(string) return not bool(string) print (is_allowed("abyzABYZ0099")) # Out: 'True' print (is_allowed("#*@#$%^")) # Out: 'False'

You can also adapt the expression line from [^a-zA-Z0-9.] to [^a-z0-9.], to disallow uppercase letters for example. Partial credit: http://stackoverflow.com/a/1325265/2697955

Section 75.8: Splitting a string using regular expressions You can also use regular expressions to split a string. For example, import re data = re.split(r'\s+', 'James 94 Samantha 417 Scarlett 74') print( data ) # Output: ['James', '94', 'Samantha', '417', 'Scarlett', '74']

Section 75.9: Grouping Grouping is done with parentheses. Calling group() returns a string formed of the matching parenthesized subgroups. match.group() # Group without argument returns the entire match found # Out: '123' match.group(0) # Specifying 0 gives the same result as specifying no argument # Out: '123'

Arguments can also be provided to group() to fetch a particular subgroup. From the docs: If there is a single argument, the result is a single string; if there are multiple arguments, the result is a tuple with one item per argument. Calling groups() on the other hand, returns a list of tuples containing the subgroups.

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sentence = "This is a phone number 672-123-456-9910" pattern = r".*(phone).*?([\d-]+)" match = re.match(pattern, sentence) match.groups() # The entire match as a list of tuples of the paranthesized subgroups # Out: ('phone', '672-123-456-9910') m.group() # The entire match as a string # Out: 'This is a phone number 672-123-456-9910' m.group(0) # The entire match as a string # Out: 'This is a phone number 672-123-456-9910' m.group(1) # Out: 'phone'

# The first parenthesized subgroup.

m.group(2) # The second parenthesized subgroup. # Out: '672-123-456-9910' m.group(1, 2) # Multiple arguments give us a tuple. # Out: ('phone', '672-123-456-9910')

Named groups match = re.search(r'My name is (?P[A-Za-z ]+)', 'My name is John Smith') match.group('name') # Out: 'John Smith' match.group(1) # Out: 'John Smith'

Creates a capture group that can be referenced by name as well as by index. Non-capturing groups Using (?:) creates a group, but the group isn't captured. This means you can use it as a group, but it won't pollute your "group space". re.match(r'(\d+)(\+(\d+))?', '11+22').groups() # Out: ('11', '+22', '22') re.match(r'(\d+)(?:\+(\d+))?', '11+22').groups() # Out: ('11', '22')

This example matches 11+22 or 11, but not 11+. This is since the + sign and the second term are grouped. On the other hand, the + sign isn't captured.

Section 75.10: Escaping Special Characters Special characters (like the character class brackets [ and ] below) are not matched literally: match = re.search(r'[b]', 'a[b]c') match.group() # Out: 'b'

By escaping the special characters, they can be matched literally: match = re.search(r'\[b\]', 'a[b]c') match.group()

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# Out: '[b]'

The re.escape() function can be used to do this for you: re.escape('a[b]c') # Out: 'a\\[b\\]c' match = re.search(re.escape('a[b]c'), 'a[b]c') match.group() # Out: 'a[b]c'

The re.escape() function escapes all special characters, so it is useful if you are composing a regular expression based on user input: username = 'A.C.' # suppose this came from the user re.findall(r'Hi {}!'.format(username), 'Hi A.C.! Hi ABCD!') # Out: ['Hi A.C.!', 'Hi ABCD!'] re.findall(r'Hi {}!'.format(re.escape(username)), 'Hi A.C.! Hi ABCD!') # Out: ['Hi A.C.!']

Section 75.11: Match an expression only in specific locations Often you want to match an expression only in specific places (leaving them untouched in others, that is). Consider the following sentence: An apple a day keeps the doctor away (I eat an apple everyday).

Here the "apple" occurs twice which can be solved with so called backtracking control verbs which are supported by the newer regex module. The idea is: forget_this | or this | and this as well | (but keep this)

With our apple example, this would be: import regex as re string = "An apple a day keeps rx = re.compile(r''' \([^()]*\) (*SKIP)(*FAIL) | apple ''', re.VERBOSE) apples = rx.findall(string) print(apples) # only one

the doctor away (I eat an apple everyday)." # match anything in parentheses and "throw it away" # or # match an apple

This matches "apple" only when it can be found outside of the parentheses.

Here's how it works: While looking from left to right, the regex engine consumes everything to the left, the (*SKIP) acts as an "always-true-assertion". Afterwards, it correctly fails on (*FAIL) and backtracks. Now it gets to the point of (*SKIP) from right to left (aka while backtracking) where it is forbidden to go any further to the left. Instead, the engine is told to throw away anything to the left and jump to the point where the (*SKIP) was invoked.

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Section 75.12: Iterating over matches using `re.finditer` You can use re.finditer to iterate over all matches in a string. This gives you (in comparison to re.findall extra information, such as information about the match location in the string (indexes): import re text = 'You can try to find an ant in this string' pattern = 'an?\w' # find 'an' either with or without a following word character for match in re.finditer(pattern, text): # Start index of match (integer) sStart = match.start() # Final index of match (integer) sEnd = match.end() # Complete match (string) sGroup = match.group() # Print match print('Match "{}" found at: [{},{}]'.format(sGroup, sStart,sEnd))

Result: Match "an" found at: [5,7] Match "an" found at: [20,22] Match "ant" found at: [23,26]

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Chapter 76: Copying data Section 76.1: Copy a dictionary A dictionary object has the method copy. It performs a shallow copy of the dictionary. >>> d1 = {1:[]} >>> d2 = d1.copy() >>> d1 is d2 False >>> d1[1] is d2[1] True

Section 76.2: Performing a shallow copy A shallow copy is a copy of a collection without performing a copy of its elements. >>> import copy >>> c = [[1,2]] >>> d = copy.copy(c) >>> c is d False >>> c[0] is d[0] True

Section 76.3: Performing a deep copy If you have nested lists, it is desirable to clone the nested lists as well. This action is called deep copy. >>> import copy >>> c = [[1,2]] >>> d = copy.deepcopy(c) >>> c is d False >>> c[0] is d[0] False

Section 76.4: Performing a shallow copy of a list You can create shallow copies of lists using slices. >>> l1 = [1,2,3] >>> l2 = l1[:] >>> l2 [1,2,3] >>> l1 is l2 False

# Perform the shallow copy.

Section 76.5: Copy a set Sets also have a copymethod. You can use this method to perform a shallow copy. >>> s1 = {()} >>> s2 = s1.copy() >>> s1 is s2

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False >>> s2.add(3) >>> s1 {[]} >>> s2 {3,[]}

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Chapter 77: Context Managers (“with” Statement) While Python's context managers are widely used, few understand the purpose behind their use. These statements, commonly used with reading and writing files, assist the application in conserving system memory and improve resource management by ensuring specific resources are only in use for certain processes. This topic explains and demonstrates the use of Python's context managers.

Section 77.1: Introduction to context managers and the with statement A context manager is an object that is notified when a context (a block of code) starts and ends. You commonly use one with the with statement. It takes care of the notifying. For example, file objects are context managers. When a context ends, the file object is closed automatically: open_file = open(filename) with open_file: file_contents = open_file.read() # the open_file object has automatically been closed.

The above example is usually simplified by using the as keyword: with open(filename) as open_file: file_contents = open_file.read() # the open_file object has automatically been closed.

Anything that ends execution of the block causes the context manager's exit method to be called. This includes exceptions, and can be useful when an error causes you to prematurely exit from an open file or connection. Exiting a script without properly closing files/connections is a bad idea, that may cause data loss or other problems. By using a context manager you can ensure that precautions are always taken to prevent damage or loss in this way. This feature was added in Python 2.5.

Section 77.2: Writing your own context manager A context manager is any object that implements two magic methods __enter__() and __exit__() (although it can implement other methods as well): class AContextManager(): def __enter__(self): print("Entered") # optionally return an object return "A-instance" def __exit__(self, exc_type, exc_value, traceback): print("Exited" + (" (with an exception)" if exc_type else "")) # return True if you want to suppress the exception

If the context exits with an exception, the information about that exception will be passed as a triple exc_type, exc_value, traceback (these are the same variables as returned by the sys.exc_info() function). If the context

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exits normally, all three of these arguments will be None. If an exception occurs and is passed to the __exit__ method, the method can return True in order to suppress the exception, or the exception will be re-raised at the end of the __exit__ function. with AContextManager() as a: print("a is %r" % a) # Entered # a is 'A-instance' # Exited with AContextManager() as a: print("a is %d" % a) # Entered # Exited (with an exception) # Traceback (most recent call last): # File "", line 2, in # TypeError: %d format: a number is required, not str

Note that in the second example even though an exception occurs in the middle of the body of the with-statement, the __exit__ handler still gets executed, before the exception propagates to the outer scope. If you only need an __exit__ method, you can return the instance of the context manager: class MyContextManager: def __enter__(self): return self def __exit__(self): print('something')

Section 77.3: Writing your own contextmanager using generator syntax It is also possible to write a context manager using generator syntax thanks to the contextlib.contextmanager decorator: import contextlib @contextlib.contextmanager def context_manager(num): print('Enter') yield num + 1 print('Exit') with context_manager(2) as cm: # the following instructions are run when the 'yield' point of the context # manager is reached. # 'cm' will have the value that was yielded print('Right in the middle with cm = {}'.format(cm))

produces: Enter Right in the middle with cm = 3 Exit

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The decorator simplifies the task of writing a context manager by converting a generator into one. Everything before the yield expression becomes the __enter__ method, the value yielded becomes the value returned by the generator (which can be bound to a variable in the with statement), and everything after the yield expression becomes the __exit__ method. If an exception needs to be handled by the context manager, a try..except..finally-block can be written in the generator and any exception raised in the with-block will be handled by this exception block. @contextlib.contextmanager def error_handling_context_manager(num): print("Enter") try: yield num + 1 except ZeroDivisionError: print("Caught error") finally: print("Cleaning up") print("Exit") with error_handling_context_manager(-1) as cm: print("Dividing by cm = {}".format(cm)) print(2 / cm)

This produces: Enter Dividing by cm = 0 Caught error Cleaning up Exit

Section 77.4: Multiple context managers You can open several content managers at the same time: with open(input_path) as input_file, open(output_path, 'w') as output_file: # do something with both files. # e.g. copy the contents of input_file into output_file for line in input_file: output_file.write(line + '\n')

It has the same effect as nesting context managers: with open(input_path) as input_file: with open(output_path, 'w') as output_file: for line in input_file: output_file.write(line + '\n')

Section 77.5: Assigning to a target Many context managers return an object when entered. You can assign that object to a new name in the with statement. For example, using a database connection in a with statement could give you a cursor object: GoalKicker.com – Python® Notes for Professionals

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with database_connection as cursor: cursor.execute(sql_query)

File objects return themselves, this makes it possible to both open the file object and use it as a context manager in one expression: with open(filename) as open_file: file_contents = open_file.read()

Section 77.6: Manage Resources class File(): def __init__(self, filename, mode): self.filename = filename self.mode = mode def __enter__(self): self.open_file = open(self.filename, self.mode) return self.open_file def __exit__(self, *args): self.open_file.close() __init__() method sets up the object, in this case setting up the file name and mode to open file. __enter__()

opens and returns the file and __exit__() just closes it. Using these magic methods (__enter__, __exit__) allows you to implement objects which can be used easily with the with statement. Use File class: for _ in range(10000): with File('foo.txt', 'w') as f: f.write('foo')

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Chapter 78: The __name__ special variable The __name__ special variable is used to check whether a file has been imported as a module or not, and to identify a function, class, module object by their __name__ attribute.

Section 78.1: __name__ == '__main__' The special variable __name__ is not set by the user. It is mostly used to check whether or not the module is being run by itself or run because an import was performed. To avoid your module to run certain parts of its code when it gets imported, check if __name__ == '__main__'. Let module_1.py be just one line long: import module2.py

And let's see what happens, depending on module2.py Situation 1 module2.py print('hello')

Running module1.py will print hello Running module2.py will print hello Situation 2 module2.py if __name__ == '__main__': print('hello')

Running module1.py will print nothing Running module2.py will print hello

Section 78.2: Use in logging When configuring the built-in logging functionality, a common pattern is to create a logger with the __name__ of the current module: logger = logging.getLogger(__name__)

This means that the fully-qualified name of the module will appear in the logs, making it easier to see where messages have come from.

Section 78.3: function_class_or_module.__name__ The special attribute __name__ of a function, class or module is a string containing its name. import os

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class C: pass def f(x): x += 2 return x

print(f) # print(f.__name__) # f print(C) # print(C.__name__) # C print(os) # print(os.__name__) # os

The __name__ attribute is not, however, the name of the variable which references the class, method or function, rather it is the name given to it when defined. def f(): pass print(f.__name__) # f - as expected g = f print(g.__name__) # f - even though the variable is named g, the function is still named f

This can be used, among others, for debugging: def enter_exit_info(func): def wrapper(*arg, **kw): print '-- entering', func.__name__ res = func(*arg, **kw) print '-- exiting', func.__name__ return res return wrapper @enter_exit_info def f(x): print 'In:', x res = x + 2 print 'Out:', res return res a = f(2) # Outputs: # -- entering f # In: 2 # Out: 4 # -- exiting f

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Chapter 79: Checking Path Existence and Permissions Parameter Details os.F_OK Value to pass as the mode parameter of access() to test the existence of path. os.R_OK

Value to include in the mode parameter of access() to test the readability of path.

os.W_OK

Value to include in the mode parameter of access() to test the writability of path.

os.X_OK

Value to include in the mode parameter of access() to determine if path can be executed.

Section 79.1: Perform checks using os.access os.access is much better solution to check whether directory exists and it's accessible for reading and writing. import os path = "/home/myFiles/directory1" ## Check if path exists os.access(path, os.F_OK) ## Check if path is Readable os.access(path, os.R_OK) ## Check if path is Writable os.access(path, os.W_OK) ## Check if path is Executable os.access(path, os.E_OK)

also it's possible to perform all checks together os.access(path, os.F_OK & os.R_OK & os.W_OK & os.E_OK)

All the above returns True if access is allowed and False if not allowed. These are available on unix and windows.

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Chapter 80: Creating Python packages Section 80.1: Introduction Every package requires a setup.py file which describes the package. Consider the following directory structure for a simple package: +-- package_name | | | +-- __init__.py | +-- setup.py

The __init__.py contains only the line def foo(): return 100. The following setup.py will define the package: from setuptools import setup

setup( name='package_name', version='0.1', description='Package Description', url='http://example.com', install_requires=[], packages=['package_name'],

# # # # # # # #

package name version short description package URL list of packages this package depends on. List of module names that installing this package will provide.

)

virtualenv is great to test package installs without modifying your other Python environments: $ virtualenv .virtualenv ... $ source .virtualenv/bin/activate $ python setup.py install running install ... Installed .../package_name-0.1-....egg ... $ python >>> import package_name >>> package_name.foo() 100

Section 80.2: Uploading to PyPI Once your setup.py is fully functional (see Introduction), it is very easy to upload your package to PyPI. Setup a .pypirc File This file stores logins and passwords to authenticate your accounts. It is typically stored in your home directory. # .pypirc file

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[distutils] index-servers = pypi pypitest [pypi] repository=https://pypi.python.org/pypi username=your_username password=your_password [pypitest] repository=https://testpypi.python.org/pypi username=your_username password=your_password

It is safer to use twine for uploading packages, so make sure that is installed. $ pip install twine

Register and Upload to testpypi (optional) Note: PyPI does not allow overwriting uploaded packages, so it is prudent to first test your deployment on a dedicated test server, e.g. testpypi. This option will be discussed. Consider a versioning scheme for your package prior to uploading such as calendar versioning or semantic versioning. Either log in, or create a new account at testpypi. Registration is only required the first time, although registering more than once is not harmful. $ python setup.py register -r pypitest

While in the root directory of your package: $ twine upload dist/* -r pypitest

Your package should now be accessible through your account. Testing Make a test virtual environment. Try to pip install your package from either testpypi or PyPI. # Using virtualenv $ mkdir testenv $ cd testenv $ virtualenv .virtualenv ... $ source .virtualenv/bin/activate # Test from testpypi (.virtualenv) pip install --verbose --extra-index-url https://testpypi.python.org/pypi package_name ... # Or test from PyPI (.virtualenv) $ pip install package_name ... (.virtualenv) $ python Python 3.5.1 (default, Jan 27 2016, 19:16:39) [GCC 4.2.1 Compatible Apple LLVM 7.0.2 (clang-700.1.81)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> import package_name

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>>> package_name.foo() 100

If successful, your package is least importable. You might consider testing your API as well before your final upload to PyPI. If you package failed during testing, do not worry. You can still fix it, re-upload to testpypi and test again. Register and Upload to PyPI Make sure twine is installed: $ pip install twine

Either log in, or create a new account at PyPI. $ python setup.py register -r pypi $ twine upload dist/*

That's it! Your package is now live. If you discover a bug, simply upload a new version of your package. Documentation Don't forget to include at least some kind of documentation for your package. PyPi takes as the default formatting language reStructuredText. Readme If your package doesn't have a big documentation, include what can help other users in README.rst file. When the file is ready, another one is needed to tell PyPi to show it. Create setup.cfg file and put these two lines in it: [metadata] description-file = README.rst

Note that if you try to put Markdown file into your package, PyPi will read it as a pure text file without any formatting. Licensing It's often more than welcome to put a LICENSE.txt file in your package with one of the OpenSource licenses to tell users if they can use your package for example in commercial projects or if your code is usable with their license. In more readable way some licenses are explained at TL;DR.

Section 80.3: Making package executable If your package isn't only a library, but has a piece of code that can be used either as a showcase or a standalone application when your package is installed, put that piece of code into __main__.py file. Put the __main__.py in the package_name folder. This way you will be able to run it directly from console: python -m package_name

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If there's no __main__.py file available, the package won't run with this command and this error will be printed: python: No module named package_name.__main__; 'package_name' is a package and cannot be directly executed.

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Chapter 81: Usage of "pip" module: PyPI Package Manager Sometimes you may need to use pip package manager inside python eg. when some imports may raise ImportError and you want to handle the exception. If you unpack on Windows Python_root/Scripts/pip.exeinside is stored __main__.py file, where main class from pip package is imported.

This means pip package is used whenever you use pip executable. For usage of pip as executable see: pip: PyPI Package Manager

Section 81.1: Example use of commands import pip command = 'install' parameter = 'selenium' second_param = 'numpy' # You can give as many package names as needed switch = '--upgrade' pip.main([command, parameter, second_param, switch])

Only needed parameters are obligatory, so both pip.main(['freeze']) and pip.main(['freeze', '', '']) are acceptable. Batch install It is possible to pass many package names in one call, but if one install/upgrade fails, whole installation process stops and ends with status '1'. import pip installed = pip.get_installed_distributions() list = [] for i in installed: list.append(i.key) pip.main(['install']+list+['--upgrade'])

If you don't want to stop when some installs fail, call installation in loop. for i in installed: pip.main(['install']+i.key+['--upgrade'])

Section 81.2: Handling ImportError Exception When you use python file as module there is no need always check if package is installed but it is still useful for scripts. if __name__ == '__main__': try: import requests except ImportError: print("To use this module you need 'requests' module") t = input('Install requests? y/n: ') if t == 'y':

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import pip pip.main(['install', 'requests']) import requests import os import sys pass else: import os import sys print('Some functionality can be unavailable.') else: import requests import os import sys

Section 81.3: Force install Many packages for example on version 3.4 would run on 3.6 just fine, but if there are no distributions for specific platform, they can't be installed, but there is workaround. In .whl files (known as wheels) naming convention decide whether you can install package on specified platform. Eg. scikit_learn‑0.18.1‑cp36‑cp36m‑win_amd64.whl[package_name]-[version]-[python interpreter]-[python-

interpreter]-[Operating System].whl. If name of wheel file is changed, so platform does match, pip tries to install package even if platform or python version does not match. Removing platform or interpreter from name will rise an error in newest version of pip module kjhfkjdf.whl is not a valid wheel filename.. Alternatively .whl file can be unpacked using an archiver as 7-zip. - It usually contains distribution meta folder and folder with source files. These source files can be simply unpacked to site-packages directory unless this wheel contain installation script, if so, it has to be run first.

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Chapter 82: pip: PyPI Package Manager pip is the most widely-used package manager for the Python Package Index, installed by default with recent versions of Python.

Section 82.1: Install Packages To install the latest version of a package named SomePackage: $ pip install SomePackage

To install a specific version of a package: $ pip install SomePackage==1.0.4

To specify a minimum version to install for a package: $ pip install SomePackage>=1.0.4

If commands shows permission denied error on Linux/Unix then use sudo with the commands

Install from requirements files $ pip install -r requirements.txt

Each line of the requirements file indicates something to be installed, and like arguments to pip install, Details on the format of the files are here: Requirements File Format. After install the package you can check it using freeze command: $ pip freeze

Section 82.2: To list all packages installed using `pip` To list installed packages: $ pip list # example output docutils (0.9.1) Jinja2 (2.6) Pygments (1.5) Sphinx (1.1.2)

To list outdated packages, and show the latest version available: $ pip list --outdated # example output docutils (Current: 0.9.1 Latest: 0.10) Sphinx (Current: 1.1.2 Latest: 1.1.3)

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$ pip install --upgrade SomePackage

will upgrade package SomePackage and all its dependencies. Also, pip automatically removes older version of the package before upgrade. To upgrade pip itself, do $ pip install --upgrade pip

on Unix or $ python -m pip install --upgrade pip

on Windows machines.

Section 82.4: Uninstall Packages To uninstall a package: $ pip uninstall SomePackage

Section 82.5: Updating all outdated packages on Linux pip doesn't current contain a flag to allow a user to update all outdated packages in one shot. However, this can be

accomplished by piping commands together in a Linux environment: pip list --outdated --local | grep -v '^\-e' | cut -d = -f 1

| xargs -n1 pip install -U

This command takes all packages in the local virtualenv and checks if they are outdated. From that list, it gets the package name and then pipes that to a pip install -U command. At the end of this process, all local packages should be updated.

Section 82.6: Updating all outdated packages on Windows pip doesn't current contain a flag to allow a user to update all outdated packages in one shot. However, this can be

accomplished by piping commands together in a Windows environment: for /F "delims= " %i in ('pip list --outdated --local') do pip install -U %i

This command takes all packages in the local virtualenv and checks if they are outdated. From that list, it gets the package name and then pipes that to a pip install -U command. At the end of this process, all local packages should be updated.

Section 82.7: Create a requirements.txt file of all packages on the system pip assists in creating requirements.txt files by providing the freeze option. pip freeze > requirements.txt

This will save a list of all packages and their version installed on the system to a file named requirements.txt in the current folder. GoalKicker.com – Python® Notes for Professionals

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Section 82.8: Using a certain Python version with pip If you have both Python 3 and Python 2 installed, you can specify which version of Python you would like pip to use. This is useful when packages only support Python 2 or 3 or when you wish to test with both. If you want to install packages for Python 2, run either: pip install [package]

or: pip2 install [package]

If you would like to install packages for Python 3, do: pip3 install [package]

You can also invoke installation of a package to a specific python installation with: \path\to\that\python.exe -m pip install some_package # on Windows OR /usr/bin/python25 -m pip install some_package # on OS-X/Linux

On OS-X/Linux/Unix platforms it is important to be aware of the distinction between the system version of python, (which upgrading make render your system inoperable), and the user version(s) of python. You may, depending on which you are trying to upgrade, need to prefix these commands with sudo and input a password. Likewise on Windows some python installations, especially those that are a part of another package, can end up installed in system directories - those you will have to upgrade from a command window running in Admin mode if you find that it looks like you need to do this it is a very good idea to check which python installation you are trying to upgrade with a command such as python -c"import sys;print(sys.path);" or py -3.5 -c"import sys;print(sys.path);" you can also check which pip you are trying to run with pip --version

On Windows, if you have both python 2 and python 3 installed, and on your path and your python 3 is greater than 3.4 then you will probably also have the python launcher py on your system path. You can then do tricks like: py -3 -m pip install -U some_package # Install/Upgrade some_package to the latest python 3 py -3.3 -m pip install -U some_package # Install/Upgrade some_package to python 3.3 if present py -2 -m pip install -U some_package # Install/Upgrade some_package to the latest python 2 - 64 bit if present py -2.7-32 -m pip install -U some_package # Install/Upgrade some_package to python 2.7 - 32 bit if present

If you are running & maintaining multiple versions of python I would strongly recommend reading up about the python virtualenv or venv virtual environments which allow you to isolate both the version of python and which packages are present.

Section 82.9: Create a requirements.txt file of packages only in the current virtualenv pip assists in creating requirements.txt files by providing the freeze option. pip freeze --local > requirements.txt

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Global packages will not be listed.

Section 82.10: Installing packages not yet on pip as wheels Many, pure python, packages are not yet available on the Python Package Index as wheels but still install fine. However, some packages on Windows give the dreaded vcvarsall.bat not found error. The problem is that the package that you are trying to install contains a C or C++ extension and is not currently available as a pre-built wheel from the python package index, pypi, and on windows you do not have the tool chain needed to build such items. The simplest answer is to go to Christoph Gohlke's excellent site and locate the appropriate version of the libraries that you need. By appropriate in the package name a -cpNN- has to match your version of python, i.e. if you are using windows 32 bit python even on win64 the name must include -win32- and if using the 64 bit python it must include -win_amd64- and then the python version must match, i.e. for Python 34 the filename must include -cp34-, etc. this is basically the magic that pip does for you on the pypi site. Alternatively, you need to get the appropriate windows development kit for the version of python that you are using, the headers for any library that the package you are trying to build interfaces to, possibly the python headers for the version of python, etc. Python 2.7 used Visual Studio 2008, Python 3.3 and 3.4 used Visual Studio 2010, and Python 3.5+ uses Visual Studio 2015. Install “Visual C++ Compiler Package for Python 2.7”, which is available from Microsoft’s website or Install “Windows SDK for Windows 7 and .NET Framework 4” (v7.1), which is available from Microsoft’s website or Install Visual Studio 2015 Community Edition, (or any later version, when these are released), ensuring you select the options to install C & C++ support no longer the default -I am told that this can take up to 8 hours to download and install so make sure that those options are set on the first try. Then you may need to locate the header files, at the matching revision for any libraries that your desired package links to and download those to an appropriate locations. Finally you can let pip do your build - of course if the package has dependencies that you don't yet have you may also need to find the header files for them as well. Alternatives: It is also worth looking out, both on pypi or Christop's site, for any slightly earlier version of the package that you are looking for that is either pure python or pre-built for your platform and python version and possibly using those, if found, until your package does become available. Likewise if you are using the very latest version of python you may find that it takes the package maintainers a little time to catch up so for projects that really need a specific package you may have to use a slightly older python for the moment. You can also check the packages source site to see if there is a forked version that is available pre-built or as pure python and searching for alternative packages that provide the functionality that you require but are available - one example that springs to mind is the Pillow, actively maintained, drop in replacement for PIL currently not updated in 6 years and not available for python 3. Afterword, I would encourage anybody who is having this problem to go to the bug tracker for the package and add to, or raise if there isn't one already, a ticket politely requesting that the package maintainers provide a wheel on pypi for your specific combination of platform and python, if this is done then normally things will get better with time, some package maintainers don't realise that they have missed a given combination that people may be using.

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Note on Installing Pre-Releases Pip follows the rules of Semantic Versioning and by default prefers released packages over pre-releases. So if a given package has been released as V0.98 and there is also a release candidate V1.0-rc1 the default behaviour of pip install will be to install V0.98 - if you wish to install the release candidate, you are advised to test in a virtual

environment first, you can enable do so with --pip install --pre package-name or --pip install --pre -upgrade package-name. In many cases pre-releases or release candidates may not have wheels built for all platform

& version combinations so you are more likely to encounter the issues above. Note on Installing Development Versions You can also use pip to install development versions of packages from github and other locations, since such code is in flux it is very unlikely to have wheels built for it, so any impure packages will require the presence of the build tools, and they may be broken at any time so the user is strongly encouraged to only install such packages in a virtual environment. Three options exist for such installations: 1. Download compressed snapshot, most online version control systems have the option to download a compressed snapshot of the code. This can be downloaded manually and then installed with pip install path/to/downloaded/file note that for most compression formats pip will handle unpacking to a cache area, etc. 2. Let pip handle the download & install for you with: pip install URL/of/package/repository - you may also need to use the --trusted-host, --client-cert and/or --proxy flags for this to work correctly, especially in a corporate environment. e.g: > py -3.5-32 -m venv demo-pip > demo-pip\Scripts\activate.bat > python -m pip install -U pip Collecting pip Using cached pip-9.0.1-py2.py3-none-any.whl Installing collected packages: pip Found existing installation: pip 8.1.1 Uninstalling pip-8.1.1: Successfully uninstalled pip-8.1.1 Successfully installed pip-9.0.1 > pip install git+https://github.com/sphinx-doc/sphinx/ Collecting git+https://github.com/sphinx-doc/sphinx/ Cloning https://github.com/sphinx-doc/sphinx/ to c:\users\steve~1\appdata\local\temp\pip-04yn9hpp-build Collecting six>=1.5 (from Sphinx==1.7.dev20170506) Using cached six-1.10.0-py2.py3-none-any.whl Collecting Jinja2>=2.3 (from Sphinx==1.7.dev20170506) Using cached Jinja2-2.9.6-py2.py3-none-any.whl Collecting Pygments>=2.0 (from Sphinx==1.7.dev20170506) Using cached Pygments-2.2.0-py2.py3-none-any.whl Collecting docutils>=0.11 (from Sphinx==1.7.dev20170506) Using cached docutils-0.13.1-py3-none-any.whl Collecting snowballstemmer>=1.1 (from Sphinx==1.7.dev20170506) Using cached snowballstemmer-1.2.1-py2.py3-none-any.whl Collecting babel!=2.0,>=1.3 (from Sphinx==1.7.dev20170506) Using cached Babel-2.4.0-py2.py3-none-any.whl Collecting alabaster=0.7 (from Sphinx==1.7.dev20170506) Using cached alabaster-0.7.10-py2.py3-none-any.whl Collecting imagesize (from Sphinx==1.7.dev20170506) Using cached imagesize-0.7.1-py2.py3-none-any.whl Collecting requests>=2.0.0 (from Sphinx==1.7.dev20170506) Using cached requests-2.13.0-py2.py3-none-any.whl

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Collecting typing (from Sphinx==1.7.dev20170506) Using cached typing-3.6.1.tar.gz Requirement already satisfied: setuptools in f:\toolbuild\temp\demo-pip\lib\site-packages (from Sphinx==1.7.dev20170506) Collecting sphinxcontrib-websupport (from Sphinx==1.7.dev20170506) Downloading sphinxcontrib_websupport-1.0.0-py2.py3-none-any.whl Collecting colorama>=0.3.5 (from Sphinx==1.7.dev20170506) Using cached colorama-0.3.9-py2.py3-none-any.whl Collecting MarkupSafe>=0.23 (from Jinja2>=2.3->Sphinx==1.7.dev20170506) Using cached MarkupSafe-1.0.tar.gz Collecting pytz>=0a (from babel!=2.0,>=1.3->Sphinx==1.7.dev20170506) Using cached pytz-2017.2-py2.py3-none-any.whl Collecting sqlalchemy>=0.9 (from sphinxcontrib-websupport->Sphinx==1.7.dev20170506) Downloading SQLAlchemy-1.1.9.tar.gz (5.2MB) 100% |################################| 5.2MB 220kB/s Collecting whoosh>=2.0 (from sphinxcontrib-websupport->Sphinx==1.7.dev20170506) Downloading Whoosh-2.7.4-py2.py3-none-any.whl (468kB) 100% |################################| 471kB 1.1MB/s Installing collected packages: six, MarkupSafe, Jinja2, Pygments, docutils, snowballstemmer, pytz, babel, alabaster, imagesize, requests, typing, sqlalchemy, whoosh, sphinxcontrib-websupport, colorama, Sphinx Running setup.py install for MarkupSafe ... done Running setup.py install for typing ... done Running setup.py install for sqlalchemy ... done Running setup.py install for Sphinx ... done Successfully installed Jinja2-2.9.6 MarkupSafe-1.0 Pygments-2.2.0 Sphinx-1.7.dev20170506 alabaster-0.7.10 babel-2.4.0 colorama-0.3.9 docutils-0.13.1 imagesize-0.7.1 pytz-2017.2 requests-2.13.0 six-1.10.0 snowballstemmer-1.2.1 sphinxcontrib-websupport-1.0.0 sqlalchemy-1.1.9 typing-3.6.1 whoosh-2.7.4

Note the git+ prefix to the URL. 3. Clone the repository using git, mercurial or other acceptable tool, preferably a DVCS tool, and use pip install path/to/cloned/repo - this will both process any requires.text file and perform the build and setup

steps, you can manually change directory to your cloned repository and run pip install -r requires.txt and then python setup.py install to get the same effect. The big advantages of this approach is that while the initial clone operation may take longer than the snapshot download you can update to the latest with, in the case of git: git pull origin master and if the current version contains errors you can use pip uninstall package-name then use git checkout commands to move back through the repository history to earlier version(s) and re-try.

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Chapter 83: Parsing Command Line arguments Most command line tools rely on arguments passed to the program upon its execution. Instead of prompting for input, these programs expect data or specific flags (which become booleans) to be set. This allows both the user and other programs to run the Python file passing it data as it starts. This section explains and demonstrates the implementation and usage of command line arguments in Python.

Section 83.1: Hello world in argparse The following program says hello to the user. It takes one positional argument, the name of the user, and can also be told the greeting. import argparse parser = argparse.ArgumentParser() parser.add_argument('name', help='name of user' ) parser.add_argument('-g', '--greeting', default='Hello', help='optional alternate greeting' ) args = parser.parse_args() print("{greeting}, {name}!".format( greeting=args.greeting, name=args.name) ) $ python hello.py --help usage: hello.py [-h] [-g GREETING] name positional arguments: name

name of user

optional arguments: -h, --help show this help message and exit -g GREETING, --greeting GREETING optional alternate greeting $ python hello.py world Hello, world! $ python hello.py John -g Howdy Howdy, John!

For more details please read the argparse documentation.

Section 83.2: Using command line arguments with argv Whenever a Python script is invoked from the command line, the user may supply additional command line arguments which will be passed on to the script. These arguments will be available to the programmer from the system variable sys.argv ("argv" is a traditional name used in most programming languages, and it means "argument vector"). GoalKicker.com – Python® Notes for Professionals

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By convention, the first element in the sys.argv list is the name of the Python script itself, while the rest of the elements are the tokens passed by the user when invoking the script. # cli.py import sys print(sys.argv) $ python cli.py => ['cli.py'] $ python cli.py fizz => ['cli.py', 'fizz'] $ python cli.py fizz buzz => ['cli.py', 'fizz', 'buzz']

Here's another example of how to use argv. We first strip off the initial element of sys.argv because it contains the script's name. Then we combine the rest of the arguments into a single sentence, and finally print that sentence prepending the name of the currently logged-in user (so that it emulates a chat program). import getpass import sys words = sys.argv[1:] sentence = " ".join(words) print("[%s] %s" % (getpass.getuser(), sentence))

The algorithm commonly used when "manually" parsing a number of non-positional arguments is to iterate over the sys.argv list. One way is to go over the list and pop each element of it: # reverse and copy sys.argv argv = reversed(sys.argv) # extract the first element arg = argv.pop() # stop iterating when there's no more args to pop() while len(argv) > 0: if arg in ('-f', '--foo'): print('seen foo!') elif arg in ('-b', '--bar'): print('seen bar!') elif arg in ('-a', '--with-arg'): arg = arg.pop() print('seen value: {}'.format(arg)) # get the next value arg = argv.pop()

Section 83.3: Setting mutually exclusive arguments with argparse If you want two or more arguments to be mutually exclusive. You can use the function argparse.ArgumentParser.add_mutually_exclusive_group(). In the example below, either foo or bar can exist

but not both at the same time. import argparse parser = argparse.ArgumentParser() group = parser.add_mutually_exclusive_group()

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group.add_argument("-f", "--foo") group.add_argument("-b", "--bar") args = parser.parse_args() print "foo = ", args.foo print "bar = ", args.bar

If you try to run the script specifying both --foo and --bar arguments, the script will complain with the below message. error: argument -b/--bar: not allowed with argument -f/--foo

Section 83.4: Basic example with docopt docopt turns command-line argument parsing on its head. Instead of parsing the arguments, you just write the usage string for your program, and docopt parses the usage string and uses it to extract the command line arguments. """ Usage: script_name.py [-a] [-b] Options: -a Print all the things. -b Get more bees into the path. """ from docopt import docopt

if __name__ == "__main__": args = docopt(__doc__) import pprint; pprint.pprint(args)

Sample runs: $ python script_name.py Usage: script_name.py [-a] $ python script_name.py {'-a': False, '-b': False, '': 'something'} $ python script_name.py {'-a': True, '-b': False, '': 'something'} $ python script_name.py {'-a': True, '-b': True, '': 'something'}

[-b] something

something -a

-b something -a

Section 83.5: Custom parser error message with argparse You can create parser error messages according to your script needs. This is through the argparse.ArgumentParser.error function. The below example shows the script printing a usage and an error

message to stderr when --foo is given but not --bar. import argparse

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parser = argparse.ArgumentParser() parser.add_argument("-f", "--foo") parser.add_argument("-b", "--bar") args = parser.parse_args() if args.foo and args.bar is None: parser.error("--foo requires --bar. You did not specify bar.") print "foo =", args.foo print "bar =", args.bar

Assuming your script name is sample.py, and we run: python sample.py --foo ds_in_fridge The script will complain with the following: usage: sample.py [-h] [-f FOO] [-b BAR] sample.py: error: --foo requires --bar. You did not specify bar.

Section 83.6: Conceptual grouping of arguments with argparse.add_argument_group() When you create an argparse ArgumentParser() and run your program with '-h' you get an automated usage message explaining what arguments you can run your software with. By default, positional arguments and conditional arguments are separated into two categories, for example, here is a small script (example.py) and the output when you run python example.py -h. import argparse parser = argparse.ArgumentParser(description='Simple example') parser.add_argument('name', help='Who to greet', default='World') parser.add_argument('--bar_this') parser.add_argument('--bar_that') parser.add_argument('--foo_this') parser.add_argument('--foo_that') args = parser.parse_args() usage: example.py [-h] [--bar_this BAR_THIS] [--bar_that BAR_THAT] [--foo_this FOO_THIS] [--foo_that FOO_THAT] name Simple example positional arguments: name optional arguments: -h, --help --bar_this BAR_THIS --bar_that BAR_THAT --foo_this FOO_THIS --foo_that FOO_THAT

Who to greet

show this help message and exit

There are some situations where you want to separate your arguments into further conceptual sections to assist your user. For example, you may wish to have all the input options in one group, and all the output formatting options in another. The above example can be adjusted to separate the --foo_* args from the --bar_* args like so. import argparse parser = argparse.ArgumentParser(description='Simple example')

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parser.add_argument('name', help='Who to greet', default='World') # Create two argument groups foo_group = parser.add_argument_group(title='Foo options') bar_group = parser.add_argument_group(title='Bar options') # Add arguments to those groups foo_group.add_argument('--bar_this') foo_group.add_argument('--bar_that') bar_group.add_argument('--foo_this') bar_group.add_argument('--foo_that') args = parser.parse_args()

Which produces this output when python example.py -h is run: usage: example.py [-h] [--bar_this BAR_THIS] [--bar_that BAR_THAT] [--foo_this FOO_THIS] [--foo_that FOO_THAT] name Simple example positional arguments: name

Who to greet

optional arguments: -h, --help

show this help message and exit

Foo options: --bar_this BAR_THIS --bar_that BAR_THAT Bar options: --foo_this FOO_THIS --foo_that FOO_THAT

Section 83.7: Advanced example with docopt and docopt_dispatch As with docopt, with [docopt_dispatch] you craft your --help in the __doc__ variable of your entry-point module. There, you call dispatch with the doc string as argument, so it can run the parser over it. That being done, instead of handling manually the arguments (which usually ends up in a high cyclomatic if/else structure), you leave it to dispatch giving only how you want to handle the set of arguments. This is what the dispatch.on decorator is for: you give it the argument or sequence of arguments that should trigger the function, and that function will be executed with the matching values as parameters. """Run something in development or production mode. Usage: run.py run.py run.py run.py

--development --production items add items delete

""" from docopt_dispatch import dispatch @dispatch.on('--development') def development(host, port, **kwargs): print('in *development* mode')

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@dispatch.on('--production') def development(host, port, **kwargs): print('in *production* mode') @dispatch.on('items', 'add') def items_add(item, **kwargs): print('adding item...') @dispatch.on('items', 'delete') def items_delete(item, **kwargs): print('deleting item...') if __name__ == '__main__': dispatch(__doc__)

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Chapter 84: Subprocess Library Parameter args

Details A single executable, or sequence of executable and arguments - 'ls', ['ls', '-la']

shell

Run under a shell? The default shell to /bin/sh on POSIX.

cwd

Working directory of the child process.

Section 84.1: More flexibility with Popen Using subprocess.Popen give more fine-grained control over launched processes than subprocess.call. Launching a subprocess process = subprocess.Popen([r'C:\path\to\app.exe', 'arg1', '--flag', 'arg'])

The signature for Popen is very similar to the call function; however, Popen will return immediately instead of waiting for the subprocess to complete like call does. Waiting on a subprocess to complete process = subprocess.Popen([r'C:\path\to\app.exe', 'arg1', '--flag', 'arg']) process.wait()

Reading output from a subprocess process = subprocess.Popen([r'C:\path\to\app.exe'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) # This will block until process completes stdout, stderr = process.communicate() print stdout print stderr

Interactive access to running subprocesses You can read and write on stdin and stdout even while the subprocess hasn't completed. This could be useful when automating functionality in another program. Writing to a subprocess process = subprocess.Popen([r'C:\path\to\app.exe'], stdout = subprocess.PIPE, stdin = subprocess.PIPE)

process.stdin.write('line of input\n') # Write input line

= process.stdout.readline() # Read a line from stdout

# Do logic on line read.

However, if you only need one set of input and output, rather than dynamic interaction, you should use communicate() rather than directly accessing stdin and stdout.

Reading a stream from a subprocess In case you want to see the output of a subprocess line by line, you can use the following snippet: process = subprocess.Popen(, stdout=subprocess.PIPE) while process.poll() is None: output_line = process.stdout.readline()

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in the case the subcommand output do not have EOL character, the above snippet does not work. You can then read the output character by character as follows: process = subprocess.Popen(, stdout=subprocess.PIPE) while process.poll() is None: output_line = process.stdout.read(1)

The 1 specified as argument to the read method tells read to read 1 character at time. You can specify to read as many characters you want using a different number. Negative number or 0 tells to read to read as a single string until the EOF is encountered (see here). In both the above snippets, the process.poll() is None until the subprocess finishes. This is used to exit the loop once there is no more output to read. The same procedure could be applied to the stderr of the subprocess.

Section 84.2: Calling External Commands The simplest use case is using the subprocess.call function. It accepts a list as the first argument. The first item in the list should be the external application you want to call. The other items in the list are arguments that will be passed to that application. subprocess.call([r'C:\path\to\app.exe', 'arg1', '--flag', 'arg'])

For shell commands, set shell=True and provide the command as a string instead of a list. subprocess.call('echo "Hello, world"', shell=True)

Note that the two command above return only the exit status of the subprocess. Moreover, pay attention when using shell=True since it provides security issues (see here). If you want to be able to get the standard output of the subprocess, then substitute the subprocess.call with subprocess.check_output. For more advanced use, refer to this.

Section 84.3: How to create the command list argument The subprocess method that allows running commands needs the command in form of a list (at least using shell_mode=True).

The rules to create the list are not always straightforward to follow, especially with complex commands. Fortunately, there is a very helpful tool that allows doing that: shlex. The easiest way of creating the list to be used as command is the following: import shlex cmd_to_subprocess = shlex.split(command_used_in_the_shell)

A simple example: import shlex shlex.split('ls --color -l -t -r') out: ['ls', '--color', '-l', '-t', '-r']

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Chapter 85: setup.py Parameter

Usage

name

Name of your distribution.

version

Version string of your distribution.

packages

List of Python packages (that is, directories containing modules) to include. This can be specified manually, but a call to setuptools.find_packages() is typically used instead.

py_modules List of top-level Python modules (that is, single .py files) to include.

Section 85.1: Purpose of setup.py The setup script is the center of all activity in building, distributing, and installing modules using the Distutils. Its purpose is the correct installation of the software. If all you want to do is distribute a module called foo, contained in a file foo.py, then your setup script can be as simple as this: from distutils.core import setup setup(name='foo', version='1.0', py_modules=['foo'], )

To create a source distribution for this module, you would create a setup script, setup.py, containing the above code, and run this command from a terminal: python setup.py sdist

sdist will create an archive file (e.g., tarball on Unix, ZIP file on Windows) containing your setup script setup.py, and your module foo.py. The archive file will be named foo-1.0.tar.gz (or .zip), and will unpack into a directory foo-1.0. If an end-user wishes to install your foo module, all she has to do is download foo-1.0.tar.gz (or .zip), unpack it, and—from the foo-1.0 directory—run python setup.py install

Section 85.2: Using source control metadata in setup.py setuptools_scm is an officially-blessed package that can use Git or Mercurial metadata to determine the version

number of your package, and find Python packages and package data to include in it. from setuptools import setup, find_packages setup( setup_requires=['setuptools_scm'], use_scm_version=True, packages=find_packages(), include_package_data=True, )

This example uses both features; to only use SCM metadata for the version, replace the call to find_packages() with your manual package list, or to only use the package finder, remove use_scm_version=True. GoalKicker.com – Python® Notes for Professionals

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Section 85.3: Adding command line scripts to your python package Command line scripts inside python packages are common. You can organise your package in such a way that when a user installs the package, the script will be available on their path. If you had the greetings package which had the command line script hello_world.py. greetings/ greetings/ __init__.py hello_world.py

You could run that script by running: python greetings/greetings/hello_world.py

However if you would like to run it like so: hello_world.py

You can achieve this by adding scripts to your setup() in setup.py like this: from setuptools import setup setup( name='greetings', scripts=['hello_world.py'] )

When you install the greetings package now, hello_world.py will be added to your path. Another possibility would be to add an entry point: entry_points={'console_scripts': ['greetings=greetings.hello_world:main']}

This way you just have to run it like: greetings

Section 85.4: Adding installation options As seen in previous examples, basic use of this script is: python setup.py install

But there is even more options, like installing the package and have the possibility to change the code and test it without having to re-install it. This is done using: python setup.py develop

If you want to perform specific actions like compiling a Sphinx documentation or building fortran code, you can create your own option like this: cmdclasses = dict()

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class BuildSphinx(Command): """Build Sphinx documentation.""" description = 'Build Sphinx documentation' user_options = [] def initialize_options(self): pass def finalize_options(self): pass def run(self): import sphinx sphinx.build_main(['setup.py', '-b', 'html', './doc', './doc/_build/html']) sphinx.build_main(['setup.py', '-b', 'man', './doc', './doc/_build/man']) cmdclasses['build_sphinx'] = BuildSphinx setup( ... cmdclass=cmdclasses, ) initialize_options and finalize_options will be executed before and after the run function as their names

suggests it. After that, you will be able to call your option: python setup.py build_sphinx

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Chapter 86: Recursion Section 86.1: The What, How, and When of Recursion Recursion occurs when a function call causes that same function to be called again before the original function call terminates. For example, consider the well-known mathematical expression x! (i.e. the factorial operation). The factorial operation is defined for all nonnegative integers as follows: If the number is 0, then the answer is 1. Otherwise, the answer is that number times the factorial of one less than that number. In Python, a naïve implementation of the factorial operation can be defined as a function as follows: def factorial(n): if n == 0: return 1 else: return n * factorial(n - 1)

Recursion functions can be difficult to grasp sometimes, so let's walk through this step-by-step. Consider the expression factorial(3). This and all function calls create a new environment. An environment is basically just a table that maps identifiers (e.g. n, factorial, print, etc.) to their corresponding values. At any point in time, you can access the current environment using locals(). In the first function call, the only local variable that gets defined is n = 3. Therefore, printing locals() would show {'n': 3}. Since n == 3, the return value becomes n * factorial(n - 1).

At this next step is where things might get a little confusing. Looking at our new expression, we already know what n is. However, we don't yet know what factorial(n - 1) is. First, n - 1 evaluates to 2. Then, 2 is passed to factorial as the value for n. Since this is a new function call, a second environment is created to store this new n.

Let A be the first environment and B be the second environment. A still exists and equals {'n': 3}, however, B (which equals {'n': 2}) is the current environment. Looking at the function body, the return value is, again, n * factorial(n - 1). Without evaluating this expression, let's substitute it into the original return expression. By

doing this, we're mentally discarding B, so remember to substitute n accordingly (i.e. references to B's n are replaced with n - 1 which uses A's n). Now, the original return expression becomes n * ((n - 1) * factorial((n - 1) - 1)). Take a second to ensure that you understand why this is so.

Now, let's evaluate the factorial((n - 1) - 1)) portion of that. Since A's n == 3, we're passing 1 into factorial. Therefore, we are creating a new environment C which equals {'n': 1}. Again, the return value is n * factorial(n - 1). So let's replace factorial((n - 1) - 1)) of the “original” return expression similarly to how we adjusted the

original return expression earlier. The “original” expression is now n * ((n - 1) * ((n - 2) * factorial((n 2) - 1))).

Almost done. Now, we need to evaluate factorial((n - 2) - 1). This time, we're passing in 0. Therefore, this evaluates to 1. Now, let's perform our last substitution. The “original” return expression is now n * ((n - 1) * ((n - 2) * 1)). Recalling that the original return expression is evaluated under A, the expression becomes 3 * ((3 1) * ((3 - 2) * 1)). This, of course, evaluates to 6. To confirm that this is the correct answer, recall that 3! == 3 * 2 * 1 == 6. Before reading any further, be sure that you fully understand the concept of environments and how

they apply to recursion. The statement if n == 0: return 1 is called a base case. This is because, it exhibits no recursion. A base case is absolutely required. Without one, you'll run into infinite recursion. With that said, as long as you have at least one base case, you can have as many cases as you want. For example, we could have equivalently written factorial as GoalKicker.com – Python® Notes for Professionals

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follows: def factorial(n): if n == 0: return 1 elif n == 1: return 1 else: return n * factorial(n - 1)

You may also have multiple recursion cases, but we won't get into that since it's relatively uncommon and is often difficult to mentally process. You can also have “parallel” recursive function calls. For example, consider the Fibonacci sequence which is defined as follows: If the number is 0, then the answer is 0. If the number is 1, then the answer is 1. Otherwise, the answer is the sum of the previous two Fibonacci numbers. We can define this is as follows: def fib(n): if n == 0 or n == 1: return n else: return fib(n - 2) + fib(n - 1)

I won't walk through this function as thoroughly as I did with factorial(3), but the final return value of fib(5) is equivalent to the following (syntactically invalid) expression: ( fib((n - 2) - 2) + ( fib(((n - 2) - 1) - 2) + fib(((n - 2) - 1) - 1) ) ) + ( ( fib(((n - 1) - 2) - 2) + fib(((n - 1) - 2) - 1) ) + ( fib(((n - 1) - 1) - 2) + ( fib((((n - 1) - 1) - 1) - 2) + fib((((n - 1) - 1) - 1) - 1) ) ) )

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This becomes (1 + (0 + 1)) + ((0 + 1) + (1 + (0 + 1))) which of course evaluates to 5. Now, let's cover a few more vocabulary terms: A tail call is simply a recursive function call which is the last operation to be performed before returning a value. To be clear, return foo(n - 1) is a tail call, but return foo(n - 1) + 1 is not (since the addition is the last operation). Tail call optimization (TCO) is a way to automatically reduce recursion in recursive functions. Tail call elimination (TCE) is the reduction of a tail call to an expression that can be evaluated without recursion. TCE is a type of TCO. Tail call optimization is helpful for a number of reasons: The interpreter can minimize the amount of memory occupied by environments. Since no computer has unlimited memory, excessive recursive function calls would lead to a stack overflow. The interpreter can reduce the number of stack frame switches. Python has no form of TCO implemented for a number of a reasons. Therefore, other techniques are required to skirt this limitation. The method of choice depends on the use case. With some intuition, the definitions of factorial and fib can relatively easily be converted to iterative code as follows: def factorial(n): product = 1 while n > 1: product *= n n -= 1 return product def fib(n): a, b = 0, 1 while n > 0: a, b = b, a + b n -= 1 return a

This is usually the most efficient way to manually eliminate recursion, but it can become rather difficult for more complex functions. Another useful tool is Python's lru_cache decorator which can be used to reduce the number of redundant calculations. You now have an idea as to how to avoid recursion in Python, but when should you use recursion? The answer is “not often”. All recursive functions can be implemented iteratively. It's simply a matter of figuring out how to do so. However, there are rare cases in which recursion is okay. Recursion is common in Python when the expected inputs wouldn't cause a significant number of a recursive function calls. If recursion is a topic that interests you, I implore you to study functional languages such as Scheme or Haskell. In such languages, recursion is much more useful. Please note that the above example for the Fibonacci sequence, although good at showing how to apply the definition in python and later use of the lru cache, has an inefficient running time since it makes 2 recursive calls for each non base case. The number of calls to the function grows exponentially to n. Rather non-intuitively a more efficient implementation would use linear recursion: def fib(n): if n >> def failing_function(): ... raise ValueError('Example error!') >>> failing_function() Traceback (most recent call last): File "", line 1, in File "", line 2, in failing_function ValueError: Example error!

which says that a ValueError with the message 'Example error!' was raised by our failing_function(), which was executed in the interpreter. Calling code can choose to handle any and all types of exception that a call can raise: >>> try: ... failing_function() ... except ValueError: ... print('Handled the error') Handled the error

You can get hold of the exception objects by assigning them in the except... part of the exception handling code: >>> try: ... failing_function() ... except ValueError as e: ... print('Caught exception', repr(e)) Caught exception ValueError('Example error!',)

A complete list of built-in Python exceptions along with their descriptions can be found in the Python Documentation: https://docs.python.org/3.5/library/exceptions.html. And here is the full list arranged hierarchically: Exception Hierarchy.

Section 88.11: Running clean-up code with finally Sometimes, you may want something to occur regardless of whatever exception happened, for example, if you have to clean up some resources. The finally block of a try clause will happen regardless of whether any exceptions were raised.

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resource = allocate_some_expensive_resource() try: do_stuff(resource) except SomeException as e: log_error(e) raise # re-raise the error finally: free_expensive_resource(resource)

This pattern is often better handled with context managers (using the with statement).

Section 88.12: Chain exceptions with raise from In the process of handling an exception, you may want to raise another exception. For example, if you get an IOError while reading from a file, you may want to raise an application-specific error to present to the users of your

library, instead. Python 3.x Version

≥ 3.0

You can chain exceptions to show how the handling of exceptions proceeded: >>> try: 5 / 0 except ZeroDivisionError as e: raise ValueError("Division failed") from e Traceback (most recent call last): File "", line 2, in ZeroDivisionError: division by zero The above exception was the direct cause of the following exception: Traceback (most recent call last): File "", line 4, in ValueError: Division failed

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Chapter 89: Raise Custom Errors / Exceptions Python has many built-in exceptions which force your program to output an error when something in it goes wrong. However, sometimes you may need to create custom exceptions that serve your purpose. In Python, users can define such exceptions by creating a new class. This exception class has to be derived, either directly or indirectly, from Exception class. Most of the built-in exceptions are also derived from this class.

Section 89.1: Custom Exception Here, we have created a user-defined exception called CustomError which is derived from the Exception class. This new exception can be raised, like other exceptions, using the raise statement with an optional error message. class CustomError(Exception): pass x = 1 if x == 1: raise CustomError('This is custom error')

Output: Traceback (most recent call last): File "error_custom.py", line 8, in raise CustomError('This is custom error') __main__.CustomError: This is custom error

Section 89.2: Catch custom Exception This example shows how to catch custom Exception class CustomError(Exception): pass try: raise CustomError('Can you catch me ?') except CustomError as e: print ('Catched CustomError :{}'.format(e)) except Exception as e: print ('Generic exception: {}'.format(e))

Output: Catched CustomError :Can you catch me ?

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Chapter 90: Commonwealth Exceptions Here in Stack Overflow we often see duplicates talking about the same errors: "ImportError: No module named '??????', SyntaxError: invalid syntax or NameError: name '???' is not defined. This is an effort to reduce

them and to have some documentation to link to.

Section 90.1: Other Errors AssertError The assert statement exists in almost every programming language. When you do: assert condition

or: assert condition, message

It's equivalent to this: if __debug__: if not condition: raise AssertionError(message)

Assertions can include an optional message, and you can disable them when you're done debugging. Note: the built-in variable debug is True under normal circumstances, False when optimization is requested (command line option -O). Assignments to debug are illegal. The value for the built-in variable is determined when the interpreter starts. KeyboardInterrupt Error raised when the user presses the interrupt key, normally Ctrl + C or del . ZeroDivisionError You tried to calculate 1/0 which is undefined. See this example to find the divisors of a number: Python 2.x Version

≥ 2.0 Version ≤ 2.7

div = float(raw_input("Divisors of: ")) for x in xrange(div+1): #includes the number itself and zero if div/x == div//x: print x, "is a divisor of", div

Python 3.x Version

≥ 3.0

div = int(input("Divisors of: ")) for x in range(div+1): #includes the number itself and zero if div/x == div//x: print(x, "is a divisor of", div)

It raises ZeroDivisionError because the for loop assigns that value to x. Instead it should be: Python 2.x Version

≥ 2.0 Version ≤ 2.7

div = float(raw_input("Divisors of: ")) for x in xrange(1,div+1): #includes the number itself but not zero if div/x == div//x:

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print x, "is a divisor of", div

Python 3.x Version

≥ 3.0

div = int(input("Divisors of: ")) for x in range(1,div+1): #includes the number itself but not zero if div/x == div//x: print(x, "is a divisor of", div)

Section 90.2: NameError: name '???' is not defined Is raised when you tried to use a variable, method or function that is not initialized (at least not before). In other words, it is raised when a requested local or global name is not found. It's possible that you misspelt the name of the object or forgot to import something. Also maybe it's in another scope. We'll cover those with separate examples.

It's simply not defined nowhere in the code It's possible that you forgot to initialize it, especially if it is a constant foo # This variable is not defined bar() # This function is not defined

Maybe it's defined later: baz() def baz(): pass

Or it wasn't imported: #needs import math def sqrt(): x = float(input("Value: ")) return math.sqrt(x)

Python scopes and the LEGB Rule: The so-called LEGB Rule talks about the Python scopes. Its name is based on the different scopes, ordered by the correspondent priorities: Local → Enclosed → Global → Built-in.

Local: Variables not declared global or assigned in a function. Enclosing: Variables defined in a function that is wrapped inside another function. Global: Variables declared global, or assigned at the top-level of a file. Built-in: Variables preassigned in the built-in names module. As an example: for i in range(4): d = i * 2 print(d)

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d is accessible because the for loop does not mark a new scope, but if it did, we would have an error and its

behavior would be similar to: def noaccess(): for i in range(4): d = i * 2 noaccess() print(d)

Python says NameError: name 'd' is not defined

Section 90.3: TypeErrors These exceptions are caused when the type of some object should be different TypeError: [definition/method] takes ? positional arguments but ? was given A function or method was called with more (or less) arguments than the ones it can accept. Example If more arguments are given: def foo(a): return a foo(a,b,c,d) #And a,b,c,d are defined

If less arguments are given: def foo(a,b,c,d): return a += b + c + d foo(a) #And a is defined

Note: if you want use an unknown number of arguments, you can use *args or **kwargs. See *args and **kwargs

TypeError: unsupported operand type(s) for [operand]: '???' and '???' Some types cannot be operated together, depending on the operand. Example For example: + is used to concatenate and add, but you can't use any of them for both types. For instance, trying to make a set by concatenating (+ing) 'set1' and 'tuple1' gives the error. Code: set1, tuple1 = {1,2}, (3,4) a = set1 + tuple1

Some types (eg: int and string) use both + but for different things: b = 400 + 'foo'

Or they may not be even used for anything: c = ["a","b"] - [1,2]

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d = 1 + 1.0

TypeError: '???' object is not iterable/subscriptable: For an object to be iterable it can take sequential indexes starting from zero until the indexes are no longer valid and a IndexError is raised (More technically: it has to have an __iter__ method which returns an __iterator__, or which defines a __getitem__ method that does what was previously mentioned). Example Here we are saying that bar is the zeroth item of 1. Nonsense: foo = 1 bar = foo[0]

This is a more discrete version: In this example for tries to set x to amount[0], the first item in an iterable but it can't because amount is an int: amount = 10 for x in amount: print(x)

TypeError: '???' object is not callable You are defining a variable and calling it later (like what you do with a function or method) Example foo = "notAFunction" foo()

Section 90.4: Syntax Error on good code The gross majority of the time a SyntaxError which points to an uninteresting line means there is an issue on the line before it (in this example, it's a missing parenthesis): def my_print(): x = (1 + 1 print(x)

Returns File "", line 3 print(x) ^ SyntaxError: invalid syntax

The most common reason for this issue is mismatched parentheses/brackets, as the example shows. There is one major caveat for print statements in Python 3: Python 3.x Version

≥ 3.0

>>> print "hello world" File "", line 1 print "hello world" ^

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SyntaxError: invalid syntax

Because the print statement was replaced with the print() function, so you want: print("hello world")

# Note this is valid for both Py2 & Py3

Section 90.5: IndentationErrors (or indentation SyntaxErrors) In most other languages indentation is not compulsory, but in Python (and other languages: early versions of FORTRAN, Makefiles, Whitespace (esoteric language), etc.) that is not the case, what can be confusing if you come from another language, if you were copying code from an example to your own, or simply if you are new. IndentationError/SyntaxError: unexpected indent This exception is raised when the indentation level increases with no reason. Example There is no reason to increase the level here: Python 2.x Version

≥ 2.0 Version ≤ 2.7

print "This line is ok" print "This line isn't ok"

Python 3.x Version

≥ 3.0

print("This line is ok") print("This line isn't ok")

Here there are two errors: the last one and that the indentation does not match any indentation level. However just one is shown: Python 2.x Version

≥ 2.0 Version ≤ 2.7

print "This line is ok" print "This line isn't ok"

Python 3.x Version

≥ 3.0

print("This line is ok") print("This line isn't ok")

IndentationError/SyntaxError: unindent does not match any outer indentation level Appears you didn't unindent completely. Example Python 2.x Version

≥ 2.0 Version ≤ 2.7

def foo(): print "This should be part of foo()" print "ERROR!" print "This is not a part of foo()"

Python 3.x Version

≥ 3.0

print("This line is ok") print("This line isn't ok")

IndentationError: expected an indented block

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After a colon (and then a new line) the indentation level has to increase. This error is raised when that didn't happen. Example if ok: doStuff()

Note: Use the keyword pass (that makes absolutely nothing) to just put an if, else, except, class, method or definition but not say what will happen if called/condition is true (but do it later, or in the case of except: just do

nothing): def foo(): pass

IndentationError: inconsistent use of tabs and spaces in indentation Example def foo(): if ok: return "Two != Four != Tab" return "i don't care i do whatever i want"

How to avoid this error Don't use tabs. It is discouraged by PEP8, the style guide for Python. 1. Set your editor to use 4 spaces for indentation. 2. Make a search and replace to replace all tabs with 4 spaces. 3. Make sure your editor is set to display tabs as 8 spaces, so that you can realize easily that error and fix it. See this question if you want to learn more.

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Chapter 91: urllib Section 91.1: HTTP GET Python 2.x Version

≤ 2.7

Python 2 import urllib response = urllib.urlopen('http://stackoverflow.com/documentation/')

Using urllib.urlopen() will return a response object, which can be handled similar to a file. print response.code # Prints: 200

The response.code represents the http return value. 200 is OK, 404 is NotFound, etc. print response.read() '\r\n\r\n\r\n\r\nDocumentation - Stack. etc' response.read() and response.readlines() can be used to read the actual html file returned from the request.

These methods operate similarly to file.read* Python 3.x Version

≥ 3.0

Python 3 import urllib.request print(urllib.request.urlopen("http://stackoverflow.com/documentation/")) # Prints: response = urllib.request.urlopen("http://stackoverflow.com/documentation/") print(response.code) # Prints: 200 print(response.read()) # Prints: b'\r\n\r\n\r\n\r\nDocumentation - Stack Overflow

The module has been updated for Python 3.x, but use cases remain basically the same. urllib.request.urlopen will return a similar file-like object.

Section 91.2: HTTP POST To POST data pass the encoded query arguments as data to urlopen() Python 2.x Version

≤ 2.7

Python 2 import urllib query_parms = {'username':'stackoverflow', 'password':'me.me'} encoded_parms = urllib.urlencode(query_parms) response = urllib.urlopen("https://stackoverflow.com/users/login", encoded_parms) response.code # Output: 200 response.read() # Output: '\r\n\r\n\r\n\r\nLog In - Stack Overflow'

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Python 3.x Version

≥ 3.0

Python 3 import urllib query_parms = {'username':'stackoverflow', 'password':'me.me'} encoded_parms = urllib.parse.urlencode(query_parms).encode('utf-8') response = urllib.request.urlopen("https://stackoverflow.com/users/login", encoded_parms) response.code # Output: 200 response.read() # Output: b'\r\n....etc'

Section 91.3: Decode received bytes according to content type encoding The received bytes have to be decoded with the correct character encoding to be interpreted as text: Python 3.x Version

≥ 3.0

import urllib.request response = urllib.request.urlopen("http://stackoverflow.com/") data = response.read() encoding = response.info().get_content_charset() html = data.decode(encoding)

Python 2.x Version

≤ 2.7

import urllib2 response = urllib2.urlopen("http://stackoverflow.com/") data = response.read() encoding = response.info().getencoding() html = data.decode(encoding)

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Chapter 92: Web scraping with Python Web scraping is an automated, programmatic process through which data can be constantly 'scraped' off webpages. Also known as screen scraping or web harvesting, web scraping can provide instant data from any publicly accessible webpage. On some websites, web scraping may be illegal.

Section 92.1: Scraping using the Scrapy framework First you have to set up a new Scrapy project. Enter a directory where you’d like to store your code and run: scrapy startproject projectName

To scrape we need a spider. Spiders define how a certain site will be scraped. Here’s the code for a spider that follows the links to the top voted questions on StackOverflow and scrapes some data from each page (source): import scrapy class StackOverflowSpider(scrapy.Spider): name = 'stackoverflow' # each spider has a unique name start_urls = ['http://stackoverflow.com/questions?sort=votes'] specific set of urls

# the parsing starts from a

def parse(self, response): # for each request this generator yields, its response is sent to parse_question for href in response.css('.question-summary h3 a::attr(href)'): # do some scraping stuff using css selectors to find question urls full_url = response.urljoin(href.extract()) yield scrapy.Request(full_url, callback=self.parse_question) def parse_question(self, response): yield { 'title': response.css('h1 a::text').extract_first(), 'votes': response.css('.question .vote-count-post::text').extract_first(), 'body': response.css('.question .post-text').extract_first(), 'tags': response.css('.question .post-tag::text').extract(), 'link': response.url, }

Save your spider classes in the projectName\spiders directory. In this case projectName\spiders\stackoverflow_spider.py.

Now you can use your spider. For example, try running (in the project's directory): scrapy crawl stackoverflow

Section 92.2: Scraping using Selenium WebDriver Some websites don’t like to be scraped. In these cases you may need to simulate a real user working with a browser. Selenium launches and controls a web browser. from selenium import webdriver browser = webdriver.Firefox()

# launch Firefox browser

browser.get('http://stackoverflow.com/questions?sort=votes')

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title = browser.find_element_by_css_selector('h1').text

# page title (first h1 element)

questions = browser.find_elements_by_css_selector('.question-summary')

# question list

for question in questions: # iterate over questions question_title = question.find_element_by_css_selector('.summary h3 a').text question_excerpt = question.find_element_by_css_selector('.summary .excerpt').text question_vote = question.find_element_by_css_selector('.stats .vote .votes .vote-countpost').text print "%s\n%s\n%s votes\n-----------\n" % (question_title, question_excerpt, question_vote)

Selenium can do much more. It can modify browser’s cookies, fill in forms, simulate mouse clicks, take screenshots of web pages, and run custom JavaScript.

Section 92.3: Basic example of using requests and lxml to scrape some data # For Python 2 compatibility. from __future__ import print_function import lxml.html import requests

def main(): r = requests.get("https://httpbin.org") html_source = r.text root_element = lxml.html.fromstring(html_source) # Note root_element.xpath() gives a *list* of results. # XPath specifies a path to the element we want. page_title = root_element.xpath('/html/head/title/text()')[0] print(page_title) if __name__ == '__main__': main()

Section 92.4: Maintaining web-scraping session with requests It is a good idea to maintain a web-scraping session to persist the cookies and other parameters. Additionally, it can result into a performance improvement because requests.Session reuses the underlying TCP connection to a host: import requests with requests.Session() as session: # all requests through session now have User-Agent header set session.headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.103 Safari/537.36'} # set cookies session.get('http://httpbin.org/cookies/set?key=value') # get cookies response = session.get('http://httpbin.org/cookies') print(response.text)

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Section 92.5: Scraping using BeautifulSoup4 from bs4 import BeautifulSoup import requests # Use the requests module to obtain a page res = requests.get('https://www.codechef.com/problems/easy') # Create a BeautifulSoup object page = BeautifulSoup(res.text, 'lxml')

# the text field contains the source of the page

# Now use a CSS selector in order to get the table containing the list of problems datatable_tags = page.select('table.dataTable') # The problems are in the

tag, # with class "dataTable" # We extract the first tag from the list, since that's what we desire datatable = datatable_tags[0] # Now since we want problem names, they are contained in tags, which are # directly nested under tags prob_tags = datatable.select('a > b') prob_names = [tag.getText().strip() for tag in prob_tags] print prob_names

Section 92.6: Simple web content download with urllib.request The standard library module urllib.request can be used to download web content: from urllib.request import urlopen response = urlopen('http://stackoverflow.com/questions?sort=votes') data = response.read() # The received bytes should usually be decoded according the response's character set encoding = response.info().get_content_charset() html = data.decode(encoding)

A similar module is also available in Python 2.

Section 92.7: Modify Scrapy user agent Sometimes the default Scrapy user agent ("Scrapy/VERSION (+http://scrapy.org)") is blocked by the host. To change the default user agent open settings.py, uncomment and edit the following line to whatever you want. #USER_AGENT = 'projectName (+http://www.yourdomain.com)'

For example USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.103 Safari/537.36'

Section 92.8: Scraping with curl imports: from subprocess import Popen, PIPE from lxml import etree

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from io import StringIO

Downloading: user_agent = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36' url = 'http://stackoverflow.com' get = Popen(['curl', '-s', '-A', user_agent, url], stdout=PIPE) result = get.stdout.read().decode('utf8') -s: silent download -A: user agent flag

Parsing: tree = etree.parse(StringIO(result), etree.HTMLParser()) divs = tree.xpath('//div')

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Chapter 93: HTML Parsing Section 93.1: Using CSS selectors in BeautifulSoup BeautifulSoup has a limited support for CSS selectors, but covers most commonly used ones. Use SELECT() method to find multiple elements and select_one() to find a single element. Basic example: from bs4 import BeautifulSoup data = """

• item1
• item2
• item3

""" soup = BeautifulSoup(data, "html.parser") for item in soup.select("li.item"): print(item.get_text())

Prints: item1 item2 item3

Section 93.2: PyQuery pyquery is a jquery-like library for python. It has very well support for css selectors. from pyquery import PyQuery html = """ Sales

Lorem	46
Ipsum	12
Dolor	27
Sit	90

"""

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doc = PyQuery(html) title = doc('h1').text() print title table_data = [] rows = doc('#table > tr') for row in rows: name = PyQuery(row).find('td').eq(0).text() value = PyQuery(row).find('td').eq(1).text() print "%s\t

%s" % (name, value)

Section 93.3: Locate a text after an element in BeautifulSoup Imagine you have the following HTML:

Name: John Smith

And you need to locate the text "John Smith" after the label element. In this case, you can locate the label element by text and then use .next_sibling property: from bs4 import BeautifulSoup data = """

Name: John Smith

""" soup = BeautifulSoup(data, "html.parser") label = soup.find("label", text="Name:") print(label.next_sibling.strip())

Prints John Smith.

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Chapter 94: Manipulating XML Section 94.1: Opening and reading using an ElementTree Import the ElementTree object, open the relevant .xml file and get the root tag: import xml.etree.ElementTree as ET tree = ET.parse("yourXMLfile.xml") root = tree.getroot()

There are a few ways to search through the tree. First is by iteration: for child in root: print(child.tag, child.attrib)

Otherwise you can reference specific locations like a list: print(root[0][1].text)

To search for specific tags by name, use the .find or .findall: print(root.findall("myTag")) print(root[0].find("myOtherTag"))

Section 94.2: Create and Build XML Documents Import Element Tree module import xml.etree.ElementTree as ET

Element() function is used to create XML elements p=ET.Element('parent')

SubElement() function used to create sub-elements to a give element c = ET.SubElement(p, 'child1')

dump() function is used to dump xml elements. ET.dump(p) # Output will be like this #

If you want to save to a file create a xml tree with ElementTree() function and to save to a file use write() method tree = ET.ElementTree(p) tree.write("output.xml")

Comment() function is used to insert comments in xml file. comment = ET.Comment('user comment') p.append(comment) #this comment will be appended to parent element

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Section 94.3: Modifying an XML File Import Element Tree module and open xml file, get an xml element import xml.etree.ElementTree as ET tree = ET.parse('sample.xml') root=tree.getroot() element = root[0] #get first child of root element

Element object can be manipulated by changing its fields, adding and modifying attributes, adding and removing children element.set('attribute_name', 'attribute_value') #set the attribute to xml element element.text="string_text"

If you want to remove an element use Element.remove() method root.remove(element)

ElementTree.write() method used to output xml object to xml files. tree.write('output.xml')

Section 94.4: Searching the XML with XPath Starting with version 2.7 ElementTree has a better support for XPath queries. XPath is a syntax to enable you to navigate through an xml like SQL is used to search through a database. Both find and findall functions support XPath. The xml below will be used for this example

Do Androids Dream of Electric Sheep? Philip K. Dick

The Colour of Magic Terry Pratchett

The Eye of The World Robert Jordan



Searching for all books: import xml.etree.cElementTree as ET tree = ET.parse('sample.xml') tree.findall('Books/Book')

Searching for the book with title = 'The Colour of Magic': tree.find("Books/Book[Title='The Colour of Magic']") # always use '' in the right side of the comparison

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Searching for the book with id = 5: tree.find("Books/Book[@id='5']") # searches with xml attributes must have '@' before the name

Search for the second book: tree.find("Books/Book[2]") # indexes starts at 1, not 0

Search for the last book: tree.find("Books/Book[last()]") # 'last' is the only xpath function allowed in ElementTree

Search for all authors: tree.findall(".//Author") #searches with // must use a relative path

Section 94.5: Opening and reading large XML files using iterparse (incremental parsing) Sometimes we don't want to load the entire XML file in order to get the information we need. In these instances, being able to incrementally load the relevant sections and then delete them when we are finished is useful. With the iterparse function you can edit the element tree that is stored while parsing the XML. Import the ElementTree object: import xml.etree.ElementTree as ET

Open the .xml file and iterate over all the elements: for event, elem in ET.iterparse("yourXMLfile.xml"): ... do something ...

Alternatively, we can only look for specific events, such as start/end tags or namespaces. If this option is omitted (as above), only "end" events are returned: events=("start", "end", "start-ns", "end-ns") for event, elem in ET.iterparse("yourXMLfile.xml", events=events): ... do something ...

Here is the complete example showing how to clear elements from the in-memory tree when we are finished with them: for event, elem in ET.iterparse("yourXMLfile.xml", events=("start","end")): if elem.tag == "record_tag" and event == "end": print elem.text elem.clear() ... do something else ...

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Chapter 95: Python Requests Post Documentation for the Python Requests module in the context of the HTTP POST method and its corresponding Requests function

Section 95.1: Simple Post from requests import post foo = post('http://httpbin.org/post', data = {'key':'value'})

Will perform a simple HTTP POST operation. Posted data can be inmost formats, however key value pairs are most prevalent. Headers Headers can be viewed: print(foo.headers)

An example response: {'Content-Length': '439', 'X-Processed-Time': '0.000802993774414', 'X-Powered-By': 'Flask', 'Server': 'meinheld/0.6.1', 'Connection': 'keep-alive', 'Via': '1.1 vegur', 'Access-Control-AllowCredentials': 'true', 'Date': 'Sun, 21 May 2017 20:56:05 GMT', 'Access-Control-Allow-Origin': '*', 'Content-Type': 'application/json'}

Headers can also be prepared before post: headers = {'Cache-Control':'max-age=0', 'Upgrade-Insecure-Requests':'1', 'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36', 'Content-Type':'application/x-www-form-urlencoded', 'Accept':'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8', 'Referer':'https://www.groupon.com/signup', 'Accept-Encoding':'gzip, deflate, br', 'Accept-Language':'es-ES,es;q=0.8' } foo = post('http://httpbin.org/post', headers=headers, data = {'key':'value'})

Encoding Encoding can be set and viewed in much the same way: print(foo.encoding) 'utf-8' foo.encoding = 'ISO-8859-1'

SSL Verification Requests by default validates SSL certificates of domains. This can be overridden: GoalKicker.com – Python® Notes for Professionals

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foo = post('http://httpbin.org/post', data = {'key':'value'}, verify=False)

Redirection Any redirection will be followed (e.g. http to https) this can also be changed: foo = post('http://httpbin.org/post', data = {'key':'value'}, allow_redirects=False)

If the post operation has been redirected, this value can be accessed: print(foo.url)

A full history of redirects can be viewed: print(foo.history)

Section 95.2: Form Encoded Data from requests import post payload = {'key1' : 'value1', 'key2' : 'value2' } foo = post('http://httpbin.org/post', data=payload)

To pass form encoded data with the post operation, data must be structured as dictionary and supplied as the data parameter. If the data does not want to be form encoded, simply pass a string, or integer to the data parameter. Supply the dictionary to the json parameter for Requests to format the data automatically: from requests import post payload = {'key1' : 'value1', 'key2' : 'value2'} foo = post('http://httpbin.org/post', json=payload)

Section 95.3: File Upload With the Requests module, it's only necessary to provide a file handle as opposed to the contents retrieved with .read(): from requests import post files = {'file' : open('data.txt', 'rb')} foo = post('http://http.org/post', files=files)

Filename, content_type and headers can also be set: files = {'file': ('report.xls', open('report.xls', 'rb'), 'application/vnd.ms-excel', {'Expires': '0'})}

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foo = requests.post('http://httpbin.org/post', files=files)

Strings can also be sent as a file, as long they are supplied as the files parameter. Multiple Files Multiple files can be supplied in much the same way as one file: multiple_files = [ ('images', ('foo.png', open('foo.png', 'rb'), 'image/png')), ('images', ('bar.png', open('bar.png', 'rb'), 'image/png'))] foo = post('http://httpbin.org/post', files=multiple_files)

Section 95.4: Responses Response codes can be viewed from a post operation: from requests import post foo = post('http://httpbin.org/post', data={'data' : 'value'}) print(foo.status_code)

Returned Data Accessing data that is returned: foo = post('http://httpbin.org/post', data={'data' : 'value'}) print(foo.text)

Raw Responses In the instances where you need to access the underlying urllib3 response.HTTPResponse object, this can be done by the following: foo = post('http://httpbin.org/post', data={'data' : 'value'}) res = foo.raw print(res.read())

Section 95.5: Authentication Simple HTTP Authentication Simple HTTP Authentication can be achieved with the following: from requests import post foo = post('http://natas0.natas.labs.overthewire.org', auth=('natas0', 'natas0'))

This is technically short hand for the following: from requests import post from requests.auth import HTTPBasicAuth

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foo = post('http://natas0.natas.labs.overthewire.org', auth=HTTPBasicAuth('natas0', 'natas0'))

HTTP Digest Authentication HTTP Digest Authentication is done in a very similar way, Requests provides a different object for this: from requests import post from requests.auth import HTTPDigestAuth foo = post('http://natas0.natas.labs.overthewire.org', auth=HTTPDigestAuth('natas0', 'natas0'))

Custom Authentication In some cases the built in authentication mechanisms may not be enough, imagine this example: A server is configured to accept authentication if the sender has the correct user-agent string, a certain header value and supplies the correct credentials through HTTP Basic Authentication. To achieve this a custom authentication class should be prepared, subclassing AuthBase, which is the base for Requests authentication implementations: from requests.auth import AuthBase from requests.auth import _basic_auth_str from requests._internal_utils import to_native_string class CustomAuth(AuthBase): def __init__(self, secret_header, user_agent , username, password): # setup any auth-related data here self.secret_header = secret_header self.user_agent = user_agent self.username = username self.password = password def __call__(self, r): # modify and return the request r.headers['X-Secret'] = self.secret_header r.headers['User-Agent'] = self.user_agent r.headers['Authorization'] = _basic_auth_str(self.username, self.password) return r

This can then be utilized with the following code: foo = get('http://test.com/admin', auth=CustomAuth('SecretHeader', 'CustomUserAgent', 'user', 'password' ))

Section 95.6: Proxies Each request POST operation can be configured to use network proxies HTTP/S Proxies from requests import post proxies = { 'http': 'http://192.168.0.128:3128', 'https': 'http://192.168.0.127:1080', }

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foo = requests.post('http://httpbin.org/post', proxies=proxies)

HTTP Basic Authentication can be provided in this manner: proxies = {'http': 'http://user:[email protected]:312'} foo = requests.post('http://httpbin.org/post', proxies=proxies)

SOCKS Proxies The use of socks proxies requires 3rd party dependencies requests[socks], once installed socks proxies are used in a very similar way to HTTPBasicAuth: proxies = { 'http': 'socks5://user:pass@host:port', 'https': 'socks5://user:pass@host:port' } foo = requests.post('http://httpbin.org/post', proxies=proxies)

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Chapter 96: Distribution Section 96.1: py2app To use the py2app framework you must install it first. Do this by opening terminal and entering the following command: sudo easy_install -U py2app

You can also pip install the packages as: pip install py2app

Then create the setup file for your python script: py2applet --make-setup MyApplication.py

Edit the settings of the setup file to your liking, this is the default: """ This is a setup.py script generated by py2applet Usage: python setup.py py2app """ from setuptools import setup APP = ['test.py'] DATA_FILES = [] OPTIONS = {'argv_emulation': True} setup( app=APP, data_files=DATA_FILES, options={'py2app': OPTIONS}, setup_requires=['py2app'], )

To add an icon file (this file must have a .icns extension), or include images in your application as reference, change your options as shown: DATA_FILES = ['myInsertedImage.jpg'] OPTIONS = {'argv_emulation': True, 'iconfile': 'myCoolIcon.icns'}

Finally enter this into terminal: python setup.py py2app

The script should run and you will find your finished application in the dist folder. Use the following options for more customization: optimize (-O)

optimization level: -O1 for "python -O", -O2 for "python -OO", and -O0 to disable [default: -O0]

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includes (-i)

comma-separated list of modules to include

packages (-p)

comma-separated list of packages to include

extension

Bundle extension [default:.app for app, .plugin for plugin]

extra-scripts

comma-separated list of additional scripts to include in an application or plugin.

Section 96.2: cx_Freeze Install cx_Freeze from here Unzip the folder and run these commands from that directory: python setup.py build sudo python setup.py install

Create a new directory for your python script and create a "setup.py" file in the same directory with the following content: application_title = "My Application" # Use your own application name main_python_file = "my_script.py" # Your python script import sys from cx_Freeze import setup, Executable base = None if sys.platform == "win32": base = "Win32GUI" includes = ["atexit","re"] setup( name = application_title, version = "0.1", description = "Your Description", options = {"build_exe" : {"includes" : includes }}, executables = [Executable(main_python_file, base = base)])

Now run your setup.py from terminal: python setup.py bdist_mac

NOTE: On El Capitan this will need to be run as root with SIP mode disabled.

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Chapter 97: Property Objects Section 97.1: Using the @property decorator for read-write properties If you want to use @property to implement custom behavior for setting and getting, use this pattern: class Cash(object): def __init__(self, value): self.value = value @property def formatted(self): return '${:.2f}'.format(self.value) @formatted.setter def formatted(self, new): self.value = float(new[1:])

To use this: >>> wallet = Cash(2.50) >>> print(wallet.formatted) $2.50 >>> print(wallet.value) 2.5 >>> wallet.formatted = '$123.45' >>> print(wallet.formatted) $123.45 >>> print(wallet.value) 123.45

Section 97.2: Using the @property decorator The @property decorator can be used to define methods in a class which act like attributes. One example where this can be useful is when exposing information which may require an initial (expensive) lookup and simple retrieval thereafter. Given some module foobar.py: class Foo(object): def __init__(self): self.__bar = None @property def bar(self): if self.__bar is None: self.__bar = some_expensive_lookup_operation() return self.__bar

Then >>> >>> >>> 42 >>> 42

from foobar import Foo foo = Foo() print(foo.bar) # This will take some time since bar is None after initialization print(foo.bar)

# This is much faster since bar has a value now

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Section 97.3: Overriding just a getter, setter or a deleter of a property object When you inherit from a class with a property, you can provide a new implementation for one or more of the property getter, setter or deleter functions, by referencing the property object on the parent class: class BaseClass(object): @property def foo(self): return some_calculated_value() @foo.setter def foo(self, value): do_something_with_value(value)

class DerivedClass(BaseClass): @BaseClass.foo.setter def foo(self, value): do_something_different_with_value(value)

You can also add a setter or deleter where there was not one on the base class before.

Section 97.4: Using properties without decorators While using decorator syntax (with the @) is convenient, it also a bit concealing. You can use properties directly, without decorators. The following Python 3.x example shows this: class A: p = 1234 def getX (self): return self._x def setX (self, value): self._x = value def getY (self): return self._y def setY (self, value): self._y = 1000 + value

# Weird but possible

def getY2 (self): return self._y def setY2 (self, value): self._y = value def getT (self): return self._t def setT (self, value): self._t = value def getU (self): return self._u + 10000 def setU (self, value): self._u = value - 5000

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x, y, y2 = property (getX, setX), property (getY, setY), property (getY2, setY2) t = property (getT, setT) u = property (getU, setU) A.q = 5678 class B: def getZ (self): return self.z_ def setZ (self, value): self.z_ = value z = property (getZ, setZ) class C: def __init__ (self): self.offset = 1234 def getW (self): return self.w_ + self.offset def setW (self, value): self.w_ = value - self.offset w = property (getW, setW) a1 = A () a2 = A () a1.y2 = 1000 a2.y2 = 2000 a1.x = 5 a1.y = 6 a2.x = 7 a2.y = 8 a1.t = 77 a1.u = 88 print (a1.x, a1.y, a1.y2) print (a2.x, a2.y, a2.y2) print (a1.p, a2.p, a1.q, a2.q) print (a1.t, a1.u) b = B () c = C () b.z = 100100 c.z = 200200 c.w = 300300 print (a1.x, b.z, c.z, c.w) c.w = 400400 c.z = 500500 b.z = 600600

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print (a1.x, b.z, c.z, c.w)

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Chapter 98: Overloading Section 98.1: Operator overloading Below are the operators that can be overloaded in classes, along with the method definitions that are required, and an example of the operator in use within an expression. N.B. The use of other as a variable name is not mandatory, but is considered the norm. Operator

Method

Expression

+ Addition

__add__(self, other)

a1 + a2

- Subtraction

__sub__(self, other)

a1 - a2

* Multiplication

__mul__(self, other)

a1 * a2

@ Matrix Multiplication

__matmul__(self, other)

a1 @ a2 (Python 3.5)

/ Division

__div__(self, other)

a1 / a2 (Python 2 only)

/ Division

__truediv__(self, other)

a1 / a2 (Python 3)

// Floor Division

__floordiv__(self, other)

a1 // a2

% Modulo/Remainder

__mod__(self, other)

a1 % a2

** Power

__pow__(self, other[, modulo]) a1 ** a2

Bitwise Right Shift

__rshift__(self, other)

a1 >> a2

& Bitwise AND

__and__(self, other)

a1 & a2

^ Bitwise XOR

__xor__(self, other)

a1 ^ a2

| (Bitwise OR)

__or__(self, other)

a1 | a2

- Negation (Arithmetic)

__neg__(self)

-a1

+ Positive

__pos__(self)

+a1

~ Bitwise NOT

__invert__(self)

~a1

< Less than

__lt__(self, other)

a1 < a2

a2

>= Greater than or Equal to __ge__(self, other)

a1 >= a2

[index] Index operator

__getitem__(self, index)

a1[index]

in In operator

__contains__(self, other)

a2 in a1

(*args, ...) Calling

__call__(self, *args, **kwargs) a1(*args, **kwargs)

The optional parameter modulo for __pow__ is only used by the pow built-in function. Each of the methods corresponding to a binary operator has a corresponding "right" method which start with __r, for example __radd__: class A: def __init__(self, a): self.a = a def __add__(self, other): return self.a + other def __radd__(self, other): print("radd")

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return other + self.a A(1) + 2 2 + A(1)

# Out: 3 # prints radd. Out: 3

as well as a corresponding inplace version, starting with __i: class B: def __init__(self, b): self.b = b def __iadd__(self, other): self.b += other print("iadd") return self b = B(2) b.b b += 1 b.b

# Out: 2 # prints iadd # Out: 3

Since there's nothing special about these methods, many other parts of the language, parts of the standard library, and even third-party modules add magic methods on their own, like methods to cast an object to a type or checking properties of the object. For example, the builtin str() function calls the object's __str__ method, if it exists. Some of these uses are listed below. Function Casting to int

Method

Expression

__int__(self)

int(a1)

Absolute function

__abs__(self)

abs(a1)

Casting to str

__str__(self)

str(a1)

Casting to unicode

__unicode__(self)

unicode(a1) (Python 2 only)

String representation __repr__(self)

repr(a1)

Casting to bool

__nonzero__(self)

bool(a1)

String formatting

__format__(self, formatstr) "Hi {:abc}".format(a1)

Hashing

__hash__(self)

hash(a1)

Length

__len__(self)

len(a1)

Reversed

__reversed__(self)

reversed(a1)

Floor

__floor__(self)

math.floor(a1)

Ceiling

__ceil__(self)

math.ceil(a1)

There are also the special methods __enter__ and __exit__ for context managers, and many more.

Section 98.2: Magic/Dunder Methods Magic (also called dunder as an abbreviation for double-underscore) methods in Python serve a similar purpose to operator overloading in other languages. They allow a class to define its behavior when it is used as an operand in unary or binary operator expressions. They also serve as implementations called by some built-in functions. Consider this implementation of two-dimensional vectors. import math class Vector(object): # instantiation def __init__(self, x, y):

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self.x = x self.y = y # unary negation (-v) def __neg__(self): return Vector(-self.x, -self.y) # addition (v + u) def __add__(self, other): return Vector(self.x + other.x, self.y + other.y) # subtraction (v - u) def __sub__(self, other): return self + (-other) # equality (v == u) def __eq__(self, other): return self.x == other.x and self.y == other.y # abs(v) def __abs__(self): return math.hypot(self.x, self.y) # str(v) def __str__(self): return ''.format(self) # repr(v) def __repr__(self): return 'Vector({0.x}, {0.y})'.format(self)

Now it is possible to naturally use instances of the Vector class in various expressions. v = Vector(1, 4) u = Vector(2, 0) u + v print(u + v) u - v u == v u + v == v + u abs(u + v)

# # # # # #

Vector(3, 4) "" (implicit string conversion) Vector(1, -4) False True 5.0

Section 98.3: Container and sequence types It is possible to emulate container types, which support accessing values by key or index. Consider this naive implementation of a sparse list, which stores only its non-zero elements to conserve memory. class sparselist(object): def __init__(self, size): self.size = size self.data = {} # l[index] def __getitem__(self, index): if index < 0: index += self.size if index >= self.size: raise IndexError(index)

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try: return self.data[index] except KeyError: return 0.0 # l[index] = value def __setitem__(self, index, value): self.data[index] = value # del l[index] def __delitem__(self, index): if index in self.data: del self.data[index] # value in l def __contains__(self, value): return value == 0.0 or value in self.data.values() # len(l) def __len__(self): return self.size # for value in l: ... def __iter__(self): return (self[i] for i in range(self.size)) # use xrange for python2

Then, we can use a sparselist much like a regular list. l = sparselist(10 ** 6) 0 in l 10 in l

# list with 1 million elements # True # False

l[12345] = 10 10 in l l[12345]

# True # 10

for v in l: pass # 0, 0, 0, ... 10, 0, 0 ... 0

Section 98.4: Callable types class adder(object): def __init__(self, first): self.first = first # a(...) def __call__(self, second): return self.first + second add2 = adder(2) add2(1) # 3 add2(2) # 4

Section 98.5: Handling unimplemented behaviour If your class doesn't implement a specific overloaded operator for the argument types provided, it should return NotImplemented (note that this is a special constant, not the same as NotImplementedError). This will allow Python

to fall back to trying other methods to make the operation work:

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When NotImplemented is returned, the interpreter will then try the reflected operation on the other type, or some other fallback, depending on the operator. If all attempted operations return NotImplemented, the interpreter will raise an appropriate exception. For example, given x + y, if x.__add__(y) returns unimplemented, y.__radd__(x) is attempted instead. class NotAddable(object): def __init__(self, value): self.value = value def __add__(self, other): return NotImplemented

class Addable(NotAddable): def __add__(self, other): return Addable(self.value + other.value) __radd__ = __add__

As this is the reflected method we have to implement __add__ and __radd__ to get the expected behaviour in all cases; fortunately, as they are both doing the same thing in this simple example, we can take a shortcut. In use: >>> x = NotAddable(1) >>> y = Addable(2) >>> x + x Traceback (most recent call last): File "", line 1, in TypeError: unsupported operand type(s) for +: 'NotAddable' and 'NotAddable' >>> y + y

>>> z = x + y >>> z

>>> z.value 3

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Chapter 99: Polymorphism Section 99.1: Duck Typing Polymorphism without inheritance in the form of duck typing as available in Python due to its dynamic typing system. This means that as long as the classes contain the same methods the Python interpreter does not distinguish between them, as the only checking of the calls occurs at run-time. class Duck: def quack(self): print("Quaaaaaack!") def feathers(self): print("The duck has white and gray feathers.") class Person: def quack(self): print("The person imitates a duck.") def feathers(self): print("The person takes a feather from the ground and shows it.") def name(self): print("John Smith") def in_the_forest(obj): obj.quack() obj.feathers() donald = Duck() john = Person() in_the_forest(donald) in_the_forest(john)

The output is: Quaaaaaack! The duck has white and gray feathers. The person imitates a duck. The person takes a feather from the ground and shows it.

Section 99.2: Basic Polymorphism Polymorphism is the ability to perform an action on an object regardless of its type. This is generally implemented by creating a base class and having two or more subclasses that all implement methods with the same signature. Any other function or method that manipulates these objects can call the same methods regardless of which type of object it is operating on, without needing to do a type check first. In object-oriented terminology when class X extend class Y , then Y is called super class or base class and X is called subclass or derived class. class Shape: """ This is a parent class that is intended to be inherited by other classes """ def calculate_area(self): """ This method is intended to be overridden in subclasses. If a subclass doesn't implement it but it is called, NotImplemented will be raised.

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""" raise NotImplemented class Square(Shape): """ This is a subclass of the Shape class, and represents a square """ side_length = 2 # in this example, the sides are 2 units long def calculate_area(self): """ This method overrides Shape.calculate_area(). When an object of type Square has its calculate_area() method called, this is the method that will be called, rather than the parent class' version. It performs the calculation necessary for this shape, a square, and returns the result. """ return self.side_length * 2 class Triangle(Shape): """ This is also a subclass of the Shape class, and it represents a triangle """ base_length = 4 height = 3 def calculate_area(self): """ This method also overrides Shape.calculate_area() and performs the area calculation for a triangle, returning the result. """ return 0.5 * self.base_length * self.height def get_area(input_obj): """ This function accepts an input object, and will call that object's calculate_area() method. Note that the object type is not specified. It could be a Square, Triangle, or Shape object. """ print(input_obj.calculate_area()) # Create one object of each class shape_obj = Shape() square_obj = Square() triangle_obj = Triangle() # Now pass each object, one at a time, to the get_area() function and see the # result. get_area(shape_obj) get_area(square_obj) get_area(triangle_obj)

We should see this output: None 4

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6.0 What happens without polymorphism? Without polymorphism, a type check may be required before performing an action on an object to determine the correct method to call. The following counter example performs the same task as the previous code, but without the use of polymorphism, the get_area() function has to do more work. class Square: side_length = 2 def calculate_square_area(self): return self.side_length ** 2 class Triangle: base_length = 4 height = 3 def calculate_triangle_area(self): return (0.5 * self.base_length) * self.height def get_area(input_obj): # Notice the type checks that are now necessary here. These type checks # could get very complicated for a more complex example, resulting in # duplicate and difficult to maintain code. if type(input_obj).__name__ == "Square": area = input_obj.calculate_square_area() elif type(input_obj).__name__ == "Triangle": area = input_obj.calculate_triangle_area() print(area) # Create one object of each class square_obj = Square() triangle_obj = Triangle() # Now pass each object, one at a time, to the get_area() function and see the # result. get_area(square_obj) get_area(triangle_obj)

We should see this output: 4 6.0 Important Note Note that the classes used in the counter example are "new style" classes and implicitly inherit from the object class if Python 3 is being used. Polymorphism will work in both Python 2.x and 3.x, but the polymorphism counterexample code will raise an exception if run in a Python 2.x interpreter because type(input_obj).name will return "instance" instead of the class name if they do not explicitly inherit from object, resulting in area never being assigned to. GoalKicker.com – Python® Notes for Professionals

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VIDEO: Machine Learning, Data Science and Deep Learning with Python Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks

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Chapter 100: Method Overriding Section 100.1: Basic method overriding Here is an example of basic overriding in Python (for the sake of clarity and compatibility with both Python 2 and 3, using new style class and print with ()): class Parent(object): def introduce(self): print("Hello!") def print_name(self): print("Parent")

class Child(Parent): def print_name(self): print("Child")

p = Parent() c = Child() p.introduce() p.print_name() c.introduce() c.print_name() $ python basic_override.py Hello! Parent Hello! Child

When the Child class is created, it inherits the methods of the Parent class. This means that any methods that the parent class has, the child class will also have. In the example, the introduce is defined for the Child class because it is defined for Parent, despite not being defined explicitly in the class definition of Child. In this example, the overriding occurs when Child defines its own print_name method. If this method was not declared, then c.print_name() would have printed "Parent". However, Child has overridden the Parent's definition of print_name, and so now upon calling c.print_name(), the word "Child" is printed.

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Chapter 101: User-Defined Methods Section 101.1: Creating user-defined method objects User-defined method objects may be created when getting an attribute of a class (perhaps via an instance of that class), if that attribute is a user-defined function object, an unbound user-defined method object, or a class method object. class A(object): # func: A user-defined function object # # Note that func is a function object when it's defined, # and an unbound method object when it's retrieved. def func(self): pass # classMethod: A class method @classmethod def classMethod(self): pass class B(object): # unboundMeth: A unbound user-defined method object # # Parent.func is an unbound user-defined method object here, # because it's retrieved. unboundMeth = A.func a = A() b = B() print A.func # output: print a.func # output: print B.unboundMeth # output: print b.unboundMeth # output: print A.classMethod # output: print a.classMethod # output:

When the attribute is a user-defined method object, a new method object is only created if the class from which it is being retrieved is the same as, or a derived class of, the class stored in the original method object; otherwise, the original method object is used as it is. # Parent: The class stored in the original method object class Parent(object): # func: The underlying function of original method object def func(self): pass func2 = func # Child: A derived class of Parent class Child(Parent): func = Parent.func

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# AnotherClass: A different class, neither subclasses nor subclassed class AnotherClass(object): func = Parent.func print print print print

Parent.func is Parent.func Parent.func2 is Parent.func2 Child.func is Child.func AnotherClass.func is AnotherClass.func

# # # #

False, new object created False, new object created False, new object created True, original object used

Section 101.2: Turtle example The following is an example of using an user-defined function to be called multiple(∞) times in a script with ease. import turtle, time, random #tell python we need 3 different modules turtle.speed(0) #set draw speed to the fastest turtle.colormode(255) #special colormode turtle.pensize(4) #size of the lines that will be drawn def triangle(size): #This is our own function, in the parenthesis is a variable we have defined that will be used in THIS FUNCTION ONLY. This fucntion creates a right triangle turtle.forward(size) #to begin this function we go forward, the amount to go forward by is the variable size turtle.right(90) #turn right by 90 degree turtle.forward(size) #go forward, again with variable turtle.right(135) #turn right again turtle.forward(size * 1.5) #close the triangle. thanks to the Pythagorean theorem we know that this line must be 1.5 times longer than the other two(if they are equal) while(1): #INFINITE LOOP turtle.setpos(random.randint(-200, 200), random.randint(-200, 200)) #set the draw point to a random (x,y) position turtle.pencolor(random.randint(1, 255), random.randint(1, 255), random.randint(1, 255)) #randomize the RGB color triangle(random.randint(5, 55)) #use our function, because it has only one variable we can simply put a value in the parenthesis. The value that will be sent will be random between 5 - 55, end the end it really just changes ow big the triangle is. turtle.pencolor(random.randint(1, 255), random.randint(1, 255), random.randint(1, 255)) #randomize color again

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Chapter 102: String representations of class instances: __str__ and __repr__ methods Section 102.1: Motivation So you've just created your first class in Python, a neat little class that encapsulates a playing card: class Card: def __init__(self, suit, pips): self.suit = suit self.pips = pips

Elsewhere in your code, you create a few instances of this class: ace_of_spades = Card('Spades', 1) four_of_clubs = Card('Clubs', 4) six_of_hearts = Card('Hearts', 6)

You've even created a list of cards, in order to represent a "hand": my_hand = [ace_of_spades, four_of_clubs, six_of_hearts]

Now, during debugging, you want to see what your hand looks like, so you do what comes naturally and write: print(my_hand)

But what you get back is a bunch of gibberish: [, , ]

Confused, you try just printing a single card: print(ace_of_spades)

And again, you get this weird output:

Have no fear. We're about to fix this. First, however, it's important to understand what's going on here. When you wrote print(ace_of_spades) you told Python you wanted it to print information about the Card instance your code is calling ace_of_spades. And to be fair, it did. That output is comprised of two important bits: the type of the object and the object's id. The second part alone (the hexadecimal number) is enough to uniquely identify the object at the time of the print call.[1] What really went on was that you asked Python to "put into words" the essence of that object and then display it to you. A more explicit version of the same machinery might be: GoalKicker.com – Python® Notes for Professionals

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string_of_card = str(ace_of_spades) print(string_of_card)

In the first line, you try to turn your Card instance into a string, and in the second you display it. The Problem The issue you're encountering arises due to the fact that, while you told Python everything it needed to know about the Card class for you to create cards, you didn't tell it how you wanted Card instances to be converted to strings. And since it didn't know, when you (implicitly) wrote str(ace_of_spades), it gave you what you saw, a generic representation of the Card instance. The Solution (Part 1) But we can tell Python how we want instances of our custom classes to be converted to strings. And the way we do this is with the __str__ "dunder" (for double-underscore) or "magic" method. Whenever you tell Python to create a string from a class instance, it will look for a __str__ method on the class, and call it. Consider the following, updated version of our Card class: class Card: def __init__(self, suit, pips): self.suit = suit self.pips = pips def __str__(self): special_names = {1:'Ace', 11:'Jack', 12:'Queen', 13:'King'} card_name = special_names.get(self.pips, str(self.pips)) return "%s of %s" % (card_name, self.suit)

Here, we've now defined the __str__ method on our Card class which, after a simple dictionary lookup for face cards, returns a string formatted however we decide. (Note that "returns" is in bold here, to stress the importance of returning a string, and not simply printing it. Printing it may seem to work, but then you'd have the card printed when you did something like str(ace_of_spades), without even having a print function call in your main program. So to be clear, make sure that __str__ returns a string.).

The __str__ method is a method, so the first argument will be self and it should neither accept, nor be passed additional arguments. Returning to our problem of displaying the card in a more user-friendly manner, if we again run: ace_of_spades = Card('Spades', 1) print(ace_of_spades)

We'll see that our output is much better: Ace of Spades So great, we're done, right? GoalKicker.com – Python® Notes for Professionals

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Well just to cover our bases, let's double check that we've solved the first issue we encountered, printing the list of Card instances, the hand.

So we re-check the following code: my_hand = [ace_of_spades, four_of_clubs, six_of_hearts] print(my_hand)

And, to our surprise, we get those funny hex codes again: [, , ]

What's going on? We told Python how we wanted our Card instances to be displayed, why did it apparently seem to forget? The Solution (Part 2) Well, the behind-the-scenes machinery is a bit different when Python wants to get the string representation of items in a list. It turns out, Python doesn't care about __str__ for this purpose. Instead, it looks for a different method, __repr__, and if that's not found, it falls back on the "hexadecimal thing".[2] So you're saying I have to make two methods to do the same thing? One for when I want to print my card by itself and another when it's in some sort of container? No, but first let's look at what our class would be like if we were to implement both __str__ and __repr__ methods: class Card: special_names = {1:'Ace', 11:'Jack', 12:'Queen', 13:'King'} def __init__(self, suit, pips): self.suit = suit self.pips = pips def __str__(self): card_name = Card.special_names.get(self.pips, str(self.pips)) return "%s of %s (S)" % (card_name, self.suit) def __repr__(self): card_name = Card.special_names.get(self.pips, str(self.pips)) return "%s of %s (R)" % (card_name, self.suit)

Here, the implementation of the two methods __str__ and __repr__ are exactly the same, except that, to differentiate between the two methods, (S) is added to strings returned by __str__ and (R) is added to strings returned by __repr__. Note that just like our __str__ method, __repr__ accepts no arguments and returns a string. We can see now what method is responsible for each case: ace_of_spades = Card('Spades', 1) four_of_clubs = Card('Clubs', 4) six_of_hearts = Card('Hearts', 6) my_hand = [ace_of_spades, four_of_clubs, six_of_hearts]

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print(my_hand)

# [Ace of Spades (R), 4 of Clubs (R), 6 of Hearts (R)]

print(ace_of_spades)

# Ace of Spades (S)

As was covered, the __str__ method was called when we passed our Card instance to print and the __repr__ method was called when we passed a list of our instances to print. At this point it's worth pointing out that just as we can explicitly create a string from a custom class instance using str() as we did earlier, we can also explicitly create a string representation of our class with a built-in function

called repr(). For example: str_card = str(four_of_clubs) print(str_card)

# 4 of Clubs (S)

repr_card = repr(four_of_clubs) print(repr_card)

# 4 of Clubs (R)

And additionally, if defined, we could call the methods directly (although it seems a bit unclear and unnecessary): print(four_of_clubs.__str__())

# 4 of Clubs (S)

print(four_of_clubs.__repr__())

# 4 of Clubs (R)

About those duplicated functions... Python developers realized, in the case you wanted identical strings to be returned from str() and repr() you might have to functionally-duplicate methods -- something nobody likes. So instead, there is a mechanism in place to eliminate the need for that. One I snuck you past up to this point. It turns out that if a class implements the __repr__ method but not the __str__ method, and you pass an instance of that class to str() (whether implicitly or explicitly), Python will fallback on your __repr__ implementation and use that. So, to be clear, consider the following version of the Card class: class Card: special_names = {1:'Ace', 11:'Jack', 12:'Queen', 13:'King'} def __init__(self, suit, pips): self.suit = suit self.pips = pips def __repr__(self): card_name = Card.special_names.get(self.pips, str(self.pips)) return "%s of %s" % (card_name, self.suit)

Note this version only implements the __repr__ method. Nonetheless, calls to str() result in the user-friendly version: print(six_of_hearts) print(str(six_of_hearts))

# 6 of Hearts # 6 of Hearts

(implicit conversion) (explicit conversion)

as do calls to repr(): print([six_of_hearts])

#[6 of Hearts] (implicit conversion)

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print(repr(six_of_hearts))

# 6 of Hearts

(explicit conversion)

Summary In order for you to empower your class instances to "show themselves" in user-friendly ways, you'll want to consider implementing at least your class's __repr__ method. If memory serves, during a talk Raymond Hettinger said that ensuring classes implement __repr__ is one of the first things he looks for while doing Python code reviews, and by now it should be clear why. The amount of information you could have added to debugging statements, crash reports, or log files with a simple method is overwhelming when compared to the paltry, and often less-than-helpful (type, id) information that is given by default. If you want different representations for when, for example, inside a container, you'll want to implement both __repr__ and __str__ methods. (More on how you might use these two methods differently below).

Section 102.2: Both methods implemented, eval-round-trip style __repr__() class Card: special_names = {1:'Ace', 11:'Jack', 12:'Queen', 13:'King'} def __init__(self, suit, pips): self.suit = suit self.pips = pips # Called when instance is converted to a string via str() # Examples: # print(card1) # print(str(card1) def __str__(self): card_name = Card.special_names.get(self.pips, str(self.pips)) return "%s of %s" % (card_name, self.suit) # Called when instance is converted to a string via repr() # Examples: # print([card1, card2, card3]) # print(repr(card1)) def __repr__(self): return "Card(%s, %d)" % (self.suit, self.pips)

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Chapter 103: Debugging Section 103.1: Via IPython and ipdb If IPython (or Jupyter) are installed, the debugger can be invoked using: import ipdb ipdb.set_trace()

When reached, the code will exit and print: /home/usr/ook.py(3)() 1 import ipdb 2 ipdb.set_trace() ----> 3 print("Hello world!") ipdb>

Clearly, this means that one has to edit the code. There is a simpler way: from IPython.core import ultratb sys.excepthook = ultratb.FormattedTB(mode='Verbose', color_scheme='Linux', call_pdb=1)

This will cause the debugger to be called if there is an uncaught exception raised.

Section 103.2: The Python Debugger: Step-through Debugging with _pdb_ The Python Standard Library includes an interactive debugging library called pdb. pdb has extensive capabilities, the most commonly used being the ability to 'step-through' a program. To immediately enter into step-through debugging use: python -m pdb

This will start the debugger at the first line of the program. Usually you will want to target a specific section of the code for debugging. To do this we import the pdb library and use set_trace() to interrupt the flow of this troubled example code. import pdb def divide(a, b): pdb.set_trace() return a/b # What's wrong with this? Hint: 2 != 3 print divide(1, 2)

Running this program will launch the interactive debugger. python foo.py > ~/scratch/foo.py(5)divide()

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-> return a/b (Pdb)

Often this command is used on one line so it can be commented out with a single # character import pdf; pdb.set_trace()

At the (Pdb) prompt commands can be entered. These commands can be debugger commands or python. To print variables we can use p from the debugger, or python's print. (Pdb) p a 1 (Pdb) print a 1

To see list of all local variables use locals

build-in function These are good debugger commands to know: b | : set breakpoint at line *n* or function named *f*. # b 3 # b divide b: show all breakpoints. c: continue until the next breakpoint. s: step through this line (will enter a function). n: step over this line (jumps over a function). r: continue until the current function returns. l: list a window of code around this line. p : print variable named *var*. # p x q: quit debugger. bt: print the traceback of the current execution call stack up: move your scope up the function call stack to the caller of the current function down: Move your scope back down the function call stack one level step: Run the program until the next line of execution in the program, then return control back to the debugger next: run the program until the next line of execution in the current function, then return control back to the debugger return: run the program until the current function returns, then return control back to the debugger continue: continue running the program until the next breakpoint (or set_trace si called again)

The debugger can also evaluate python interactively: -> return a/b (Pdb) p a+b 3 (Pdb) [ str(m) for m in [a,b]] ['1', '2'] (Pdb) [ d for d in xrange(5)] [0, 1, 2, 3, 4]

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If any of your variable names coincide with the debugger commands, use an exclamation mark '!' before the var to explicitly refer to the variable and not the debugger command. For example, often it might so happen that you use the variable name 'c' for a counter, and you might want to print it while in the debugger. a simple 'c' command would continue execution till the next breakpoint. Instead use '!c' to print the value of the variable as follows: (Pdb) !c 4

Section 103.3: Remote debugger Sometimes you need to debug python code which is executed by another process and in this cases rpdb comes in handy. rpdb is a wrapper around pdb that re-routes stdin and stdout to a socket handler. By default it opens the debugger on port 4444 Usage: # In the Python file you want to debug. import rpdb rpdb.set_trace()

And then you need run this in terminal to connect to this process. # Call in a terminal to see the output $ nc 127.0.0.1 4444

And you will get pdb promt > /home/usr/ook.py(3)() -> print("Hello world!") (Pdb)

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Chapter 104: Reading and Writing CSV Section 104.1: Using pandas Write a CSV file from a dict or a DataFrame. import pandas as pd d = {'a': (1, 101), 'b': (2, 202), 'c': (3, 303)} pd.DataFrame.from_dict(d, orient="index") df.to_csv("data.csv")

Read a CSV file as a DataFrame and convert it to a dict: df = pd.read_csv("data.csv") d = df.to_dict()

Section 104.2: Writing a TSV file Python import csv with open('/tmp/output.tsv', 'wt') as out_file: tsv_writer = csv.writer(out_file, delimiter='\t') tsv_writer.writerow(['name', 'field']) tsv_writer.writerow(['Dijkstra', 'Computer Science']) tsv_writer.writerow(['Shelah', 'Math']) tsv_writer.writerow(['Aumann', 'Economic Sciences'])

Output file $ cat /tmp/output.tsv name field Dijkstra Computer Science Shelah Math Aumann Economic Sciences

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VIDEO: Complete Python Bootcamp: Go from zero to hero in Python 3 Learn Python like a Professional! Start from the basics and go all the way to creating your own applications and games!

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Chapter 105: Writing to CSV from String or List Parameter open ("/path/", "mode")

Details Specify the path to your CSV file

open (path, "mode")

Specify mode to open file in (read, write, etc.)

csv.writer(file, delimiter)

Pass opened CSV file here

csv.writer(file, delimiter=' ') Specify delimiter character or pattern Writing to a .csv file is not unlike writing to a regular file in most regards, and is fairly straightforward. I will, to the best of my ability, cover the easiest, and most efficient approach to the problem.

Section 105.1: Basic Write Example import csv #------ We will write to CSV in this function -----------def csv_writer(data, path): #Open CSV file whose path we passed. with open(path, "wb") as csv_file: writer = csv.writer(csv_file, delimiter=',') for line in data: writer.writerow(line)

#---- Define our list here, and call function -----------if __name__ == "__main__": """ data = our list that we want to write. Split it so we get a list of lists. """ data = ["first_name,last_name,age".split(","), "John,Doe,22".split(","), "Jane,Doe,31".split(","), "Jack,Reacher,27".split(",") ] # Path to CSV file we want to write to. path = "output.csv" csv_writer(data, path)

Section 105.2: Appending a String as a newline in a CSV file def append_to_csv(input_string): with open("fileName.csv", "a") as csv_file: csv_file.write(input_row + "\n")

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Chapter 106: Dynamic code execution with `exec` and `eval` Argument

Details expression The expression code as a string, or a code object object

The statement code as a string, or a code object

globals

The dictionary to use for global variables. If locals is not specified, this is also used for locals. If omitted, the globals() of calling scope are used.

locals

A mapping object that is used for local variables. If omitted, the one passed for globals is used instead. If both are omitted, then the globals() and locals() of the calling scope are used for globals and locals respectively.

Section 106.1: Executing code provided by untrusted user using exec, eval, or ast.literal_eval It is not possible to use eval or exec to execute code from untrusted user securely. Even ast.literal_eval is prone to crashes in the parser. It is sometimes possible to guard against malicious code execution, but it doesn't exclude the possibility of outright crashes in the parser or the tokenizer. To evaluate code by an untrusted user you need to turn to some third-party module, or perhaps write your own parser and your own virtual machine in Python.

Section 106.2: Evaluating a string containing a Python literal with ast.literal_eval If you have a string that contains Python literals, such as strings, floats etc, you can use ast.literal_eval to evaluate its value instead of eval. This has the added feature of allowing only certain syntax. >>> import ast >>> code = """(1, 2, {'foo': 'bar'})""" >>> object = ast.literal_eval(code) >>> object (1, 2, {'foo': 'bar'}) >>> type(object)

However, this is not secure for execution of code provided by untrusted user, and it is trivial to crash an interpreter with carefully crafted input >>> import ast >>> ast.literal_eval('()' * 1000000) [5] 21358 segmentation fault (core dumped)

python3

Here, the input is a string of () repeated one million times, which causes a crash in CPython parser. CPython developers do not consider bugs in parser as security issues.

Section 106.3: Evaluating statements with exec >>> code = """for i in range(5):\n >>> exec(code) Hello world! Hello world! Hello world!

print('Hello world!')"""

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Hello world! Hello world!

Section 106.4: Evaluating an expression with eval >>> >>> >>> >>> 20

expression = '5 + 3 * a' a = 5 result = eval(expression) result

Section 106.5: Precompiling an expression to evaluate it multiple times compile built-in function can be used to precompile an expression to a code object; this code object can then be

passed to eval. This will speed up the repeated executions of the evaluated code. The 3rd parameter to compile needs to be the string 'eval'. >>> code = compile('a * b + c', '', 'eval') >>> code

>>> a, b, c = 1, 2, 3 >>> eval(code) 5

Section 106.6: Evaluating an expression with eval using custom globals >>> variables = {'a': 6, 'b': 7} >>> eval('a * b', globals=variables) 42

As a plus, with this the code cannot accidentally refer to the names defined outside: >>> eval('variables') {'a': 6, 'b': 7} >>> eval('variables', globals=variables) Traceback (most recent call last): File "", line 1, in File "", line 1, in NameError: name 'variables' is not defined

Using defaultdict allows for example having undefined variables set to zero: >>> from collections import defaultdict >>> variables = defaultdict(int, {'a': 42}) >>> eval('a * c', globals=variables) # note that 'c' is not explicitly defined 0

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Chapter 107: PyInstaller - Distributing Python Code Section 107.1: Installation and Setup Pyinstaller is a normal python package. It can be installed using pip: pip install pyinstaller

Installation in Windows For Windows, pywin32 or pypiwin32 is a prerequisite. The latter is installed automatically when pyinstaller is installed using pip. Installation in Mac OS X PyInstaller works with the default Python 2.7 provided with current Mac OS X. If later versions of Python are to be used or if any major packages such as PyQT, Numpy, Matplotlib and the like are to be used, it is recommended to install them using either MacPorts or Homebrew. Installing from the archive If pip is not available, download the compressed archive from PyPI. To test the development version, download the compressed archive from the develop branch of PyInstaller Downloads page. Expand the archive and find the setup.py script. Execute python setup.py install with administrator privilege to install or upgrade PyInstaller. Verifying the installation The command pyinstaller should exist on the system path for all platforms after a successful installation. Verify it by typing pyinstaller --version in the command line. This will print the current version of pyinstaller.

Section 107.2: Using Pyinstaller In the simplest use-case, just navigate to the directory your file is in, and type: pyinstaller myfile.py

Pyinstaller analyzes the file and creates: A myfile.spec file in the same directory as myfile.py A build folder in the same directory as myfile.py A dist folder in the same directory as myfile.py Log files in the build folder The bundled app can be found in the dist folder Options There are several options that can be used with pyinstaller. A full list of the options can be found here. Once bundled your app can be run by opening 'dist\myfile\myfile.exe'.

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Section 107.3: Bundling to One Folder When PyInstaller is used without any options to bundle myscript.py , the default output is a single folder (named myscript) containing an executable named myscript (myscript.exe in windows) along with all the necessary

dependencies. The app can be distributed by compressing the folder into a zip file. One Folder mode can be explicitly set using the option -D or --onedir pyinstaller myscript.py -D

Advantages: One of the major advantages of bundling to a single folder is that it is easier to debug problems. If any modules fail to import, it can be verified by inspecting the folder. Another advantage is felt during updates. If there are a few changes in the code but the dependencies used are exactly the same, distributors can just ship the executable file (which is typically smaller than the entire folder). Disadvantages The only disadvantage of this method is that the users have to search for the executable among a large number of files. Also users can delete/modify other files which might lead to the app not being able to work correctly.

Section 107.4: Bundling to a Single File pyinstaller myscript.py -F

The options to generate a single file are -F or --onefile. This bundles the program into a single myscript.exe file. Single file executable are slower than the one-folder bundle. They are also harder to debug.

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Chapter 108: Data Visualization with Python Section 108.1: Seaborn Seaborn is a wrapper around Matplotlib that makes creating common statistical plots easy. The list of supported plots includes univariate and bivariate distribution plots, regression plots, and a number of methods for plotting categorical variables. The full list of plots Seaborn provides is in their API reference. Creating graphs in Seaborn is as simple as calling the appropriate graphing function. Here is an example of creating a histogram, kernel density estimation, and rug plot for randomly generated data. import numpy as np # numpy used to create data from plotting import seaborn as sns # common form of importing seaborn # Generate normally distributed data data = np.random.randn(1000) # Plot a histogram with both a rugplot and kde graph superimposed sns.distplot(data, kde=True, rug=True)

The style of the plot can also be controlled using a declarative syntax. # Using previously created imports and data. # Use a dark background with no grid. sns.set_style('dark')

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# Create the plot again sns.distplot(data, kde=True, rug=True)

As an added bonus, normal matplotlib commands can still be applied to Seaborn plots. Here's an example of adding axis titles to our previously created histogram. # Using previously created data and style # Access to matplotlib commands import matplotlib.pyplot as plt # Previously created plot. sns.distplot(data, kde=True, rug=True) # Set the axis labels. plt.xlabel('This is my x-axis') plt.ylabel('This is my y-axis')

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Section 108.2: Matplotlib Matplotlib is a mathematical plotting library for Python that provides a variety of different plotting functionality. The matplotlib documentation can be found here, with the SO Docs being available here. Matplotlib provides two distinct methods for plotting, though they are interchangeable for the most part: Firstly, matplotlib provides the pyplot interface, direct and simple-to-use interface that allows plotting of complex graphs in a MATLAB-like style. Secondly, matplotlib allows the user to control the different aspects (axes, lines, ticks, etc) directly using an object-based system. This is more difficult but allows complete control over the entire plot. Below is an example of using the pyplot interface to plot some generated data: import matplotlib.pyplot as plt # Generate some data for plotting. x = [0, 1, 2, 3, 4, 5, 6] y = [i**2 for i in x] # Plot the data x, y with some keyword arguments that control the plot style. # Use two different plot commands to plot both points (scatter) and a line (plot). plt.scatter(x, y, c='blue', marker='x', s=100) # Create blue markers of shape "x" and size 100 plt.plot(x, y, color='red', linewidth=2) # Create a red line with linewidth 2. # Add some text to the axes and a title. plt.xlabel('x data')

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plt.ylabel('y data') plt.title('An example plot') # Generate the plot and show to the user. plt.show()

Note that plt.show() is known to be problematic in some environments due to running matplotlib.pyplot in interactive mode, and if so, the blocking behaviour can be overridden explicitly by passing in an optional argument, plt.show(block=True), to alleviate the issue.

Section 108.3: Plotly Plotly is a modern platform for plotting and data visualization. Useful for producing a variety of plots, especially for data sciences, Plotly is available as a library for Python, R, JavaScript, Julia and, MATLAB. It can also be used as a web application with these languages. Users can install plotly library and use it offline after user authentication. The installation of this library and offline authentication is given here. Also, the plots can be made in Jupyter Notebooks as well. Usage of this library requires an account with username and password. This gives the workspace to save plots and data on the cloud. The free version of the library has some slightly limited features and designed for making 250 plots per day. The paid version has all the features, unlimited plot downloads and more private data storage. For more details, one can visit the main page here. GoalKicker.com – Python® Notes for Professionals

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For documentation and examples, one can go here A sample plot from the documentation examples: import plotly.graph_objs as go import plotly as ply # Create random data with numpy import numpy as np N = 100 random_x = np.linspace(0, 1, N) random_y0 = np.random.randn(N)+5 random_y1 = np.random.randn(N) random_y2 = np.random.randn(N)-5 # Create traces trace0 = go.Scatter( x = random_x, y = random_y0, mode = 'lines', name = 'lines' ) trace1 = go.Scatter( x = random_x, y = random_y1, mode = 'lines+markers', name = 'lines+markers' ) trace2 = go.Scatter( x = random_x, y = random_y2, mode = 'markers', name = 'markers' ) data = [trace0, trace1, trace2] ply.offline.plot(data, filename='line-mode')

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Section 108.4: MayaVI MayaVI is a 3D visualization tool for scientific data. It uses the Visualization Tool Kit or VTK under the hood. Using the power of VTK, MayaVI is capable of producing a variety of 3-Dimensional plots and figures. It is available as a separate software application and also as a library. Similar to Matplotlib, this library provides an object oriented programming language interface to create plots without having to know about VTK. MayaVI is available only in Python 2.7x series! It is hoped to be available in Python 3-x series soon! (Although some success is noticed when using its dependencies in Python 3) Documentation can be found here. Some gallery examples are found here Here is a sample plot created using MayaVI from the documentation. # Author: Gael Varoquaux # Copyright (c) 2007, Enthought, Inc. # License: BSD Style.

from numpy import sin, cos, mgrid, pi, sqrt from mayavi import mlab mlab.figure(fgcolor=(0, 0, 0), bgcolor=(1, 1, 1)) u, v = mgrid[- 0.035:pi:0.01, - 0.035:pi:0.01] X = 2 / 3. * (cos(u) * cos(2 * v) + sqrt(2) * sin(u) * cos(v)) * cos(u) / (sqrt(2) sin(2 * u) * sin(3 * v)) Y = 2 / 3. * (cos(u) * sin(2 * v) sqrt(2) * sin(u) * sin(v)) * cos(u) / (sqrt(2) - sin(2 * u) * sin(3 * v))

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Z = -sqrt(2) * cos(u) * cos(u) / (sqrt(2) - sin(2 * u) * sin(3 * v)) S = sin(u) mlab.mesh(X, Y, Z, scalars=S, colormap='YlGnBu', ) # Nice view from the front mlab.view(.0, - 5.0, 4) mlab.show()

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Chapter 109: The Interpreter (Command Line Console) Section 109.1: Getting general help If the help function is called in the console without any arguments, Python presents an interactive help console, where you can find out about Python modules, symbols, keywords and more. >>> help() Welcome to Python 3.4's help utility! If this is your first time using Python, you should definitely check out the tutorial on the Internet at http://docs.python.org/3.4/tutorial/. Enter the name of any module, keyword, or topic to get help on writing Python programs and using Python modules. To quit this help utility and return to the interpreter, just type "quit". To get a list of available modules, keywords, symbols, or topics, type "modules", "keywords", "symbols", or "topics". Each module also comes with a one-line summary of what it does; to list the modules whose name or summary contain a given string such as "spam", type "modules spam".

Section 109.2: Referring to the last expression To get the value of the last result from your last expression in the console, use an underscore _. >>> 2 + 2 4 >>> _ 4 >>> _ + 6 10

This magic underscore value is only updated when using a python expression that results in a value. Defining functions or for loops does not change the value. If the expression raises an exception there will be no changes to _. >>> "Hello, {0}".format("World") 'Hello, World' >>> _ 'Hello, World' >>> def wontchangethings(): ... pass >>> _ 'Hello, World' >>> 27 / 0 Traceback (most recent call last): File "", line 1, in ZeroDivisionError: division by zero >>> _ 'Hello, World'

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Section 109.3: Opening the Python console The console for the primary version of Python can usually be opened by typing py into your windows console or python on other platforms. $ py Python 3.4.3 (v3.4.3:9b73f1c3e601, Feb 24 2015, 22:44:40) [MSC v.1600 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>>

If you have multiple versions, then by default their executables will be mapped to python2 or python3 respectively. This of course depends on the Python executables being in your PATH.

Section 109.4: The PYTHONSTARTUP variable You can set an environment variable called PYTHONSTARTUP for Python's console. Whenever you enter the Python console, this file will be executed, allowing for you to add extra functionality to the console such as importing commonly-used modules automatically. If the PYTHONSTARTUP variable was set to the location of a file containing this: print("Welcome!")

Then opening the Python console would result in this extra output: $ py Python 3.4.3 (v3.4.3:9b73f1c3e601, Feb 24 2015, 22:44:40) [MSC v.1600 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. Welcome! >>>

Section 109.5: Command line arguments Python has a variety of command-line switches which can be passed to py. These can be found by performing py -help, which gives this output on Python 3.4:

Python Launcher usage: py [ launcher-arguments ] [ python-arguments ] script [ script-arguments ] Launcher arguments: -2 : Launch the latest Python 2.x version -3 : Launch the latest Python 3.x version -X.Y : Launch the specified Python version -X.Y-32: Launch the specified 32bit Python version The following help text is from Python: usage: G:\\Python34\\python.exe [option] ... [-c cmd | -m mod | file | -] [arg] ... Options and arguments (and corresponding environment variables): -b : issue warnings about str(bytes_instance), str(bytearray_instance) and comparing bytes/bytearray with str. (-bb: issue errors) -B : don't write .py[co] files on import; also PYTHONDONTWRITEBYTECODE=x -c cmd : program passed in as string (terminates option list)

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-d : debug output from parser; also PYTHONDEBUG=x -E : ignore PYTHON* environment variables (such as PYTHONPATH) -h : print this help message and exit (also --help) -i : inspect interactively after running script; forces a prompt even if stdin does not appear to be a terminal; also PYTHONINSPECT=x -I : isolate Python from the user's environment (implies -E and -s) -m mod : run library module as a script (terminates option list) -O : optimize generated bytecode slightly; also PYTHONOPTIMIZE=x -OO : remove doc-strings in addition to the -O optimizations -q : don't print version and copyright messages on interactive startup -s : don't add user site directory to sys.path; also PYTHONNOUSERSITE -S : don't imply 'import site' on initialization -u : unbuffered binary stdout and stderr, stdin always buffered; also PYTHONUNBUFFERED=x see man page for details on internal buffering relating to '-u' -v : verbose (trace import statements); also PYTHONVERBOSE=x can be supplied multiple times to increase verbosity -V : print the Python version number and exit (also --version) -W arg : warning control; arg is action:message:category:module:lineno also PYTHONWARNINGS=arg -x : skip first line of source, allowing use of non-Unix forms of #!cmd -X opt : set implementation-specific option file : program read from script file - : program read from stdin (default; interactive mode if a tty) arg ...: arguments passed to program in sys.argv[1:] Other environment variables: PYTHONSTARTUP: file executed on interactive startup (no default) PYTHONPATH : ';'-separated list of directories prefixed to the default module search path. The result is sys.path. PYTHONHOME : alternate directory (or ;). The default module search path uses \\lib. PYTHONCASEOK : ignore case in 'import' statements (Windows). PYTHONIOENCODING: Encoding[:errors] used for stdin/stdout/stderr. PYTHONFAULTHANDLER: dump the Python traceback on fatal errors. PYTHONHASHSEED: if this variable is set to 'random', a random value is used to seed the hashes of str, bytes and datetime objects. It can also be set to an integer in the range [0,4294967295] to get hash values with a predictable seed.

Section 109.6: Getting help about an object The Python console adds a new function, help, which can be used to get information about a function or object. For a function, help prints its signature (arguments) and its docstring, if the function has one. >>> help(print) Help on built-in function print in module builtins: print(...) print(value, ..., sep=' ', end='\n', file=sys.stdout, flush=False) Prints the values to a stream, or to sys.stdout by default. Optional keyword arguments: file: a file-like object (stream); defaults to the current sys.stdout. sep: string inserted between values, default a space. end: string appended after the last value, default a newline. flush: whether to forcibly flush the stream.

For an object, help lists the object's docstring and the different member functions which the object has.

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>>> x = 2 >>> help(x) Help on int object: class int(object) | int(x=0) -> integer | int(x, base=10) -> integer | | Convert a number or string to an integer, or return 0 if no arguments | are given. If x is a number, return x.__int__(). For floating point | numbers, this truncates towards zero. | | If x is not a number or if base is given, then x must be a string, | bytes, or bytearray instance representing an integer literal in the | given base. The literal can be preceded by '+' or '-' and be surrounded | by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. | Base 0 means to interpret the base from the string as an integer literal. | >>> int('0b100', base=0) | 4 | | Methods defined here: | | __abs__(self, /) | abs(self) | | __add__(self, value, /) | Return self+value...

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Chapter 110: *args and **kwargs Section 110.1: Using **kwargs when writing functions You can define a function that takes an arbitrary number of keyword (named) arguments by using the double star ** before a parameter name: def print_kwargs(**kwargs): print(kwargs)

When calling the method, Python will construct a dictionary of all keyword arguments and make it available in the function body: print_kwargs(a="two", b=3) # prints: "{a: "two", b=3}"

Note that the **kwargs parameter in the function definition must always be the last parameter, and it will only match the arguments that were passed in after the previous ones. def example(a, **kw): print kw example(a=2, b=3, c=4) # => {'b': 3, 'c': 4}

Inside the function body, kwargs is manipulated in the same way as a dictionary; in order to access individual elements in kwargs you just loop through them as you would with a normal dictionary: def print_kwargs(**kwargs): for key in kwargs: print("key = {0}, value = {1}".format(key, kwargs[key]))

Now, calling print_kwargs(a="two", b=1) shows the following output: print_kwargs(a = "two", b = 1) key = a, value = "two" key = b, value = 1

Section 110.2: Using *args when writing functions You can use the star * when writing a function to collect all positional (ie. unnamed) arguments in a tuple: def print_args(farg, *args): print("formal arg: %s" % farg) for arg in args: print("another positional arg: %s" % arg)

Calling method: print_args(1, "two", 3)

In that call, farg will be assigned as always, and the two others will be fed into the args tuple, in the order they were received.

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Section 110.3: Populating kwarg values with a dictionary def foobar(foo=None, bar=None): return "{}{}".format(foo, bar) values = {"foo": "foo", "bar": "bar"} foobar(**values) # "foobar"

Section 110.4: Keyword-only and Keyword-required arguments Python 3 allows you to define function arguments which can only be assigned by keyword, even without default values. This is done by using star * to consume additional positional parameters without setting the keyword parameters. All arguments after the * are keyword-only (i.e. non-positional) arguments. Note that if keyword-only arguments aren't given a default, they are still required when calling the function. def print_args(arg1, *args, keyword_required, keyword_only=True): print("first positional arg: {}".format(arg1)) for arg in args: print("another positional arg: {}".format(arg)) print("keyword_required value: {}".format(keyword_required)) print("keyword_only value: {}".format(keyword_only)) print(1, 2, 3, 4) # TypeError: print_args() missing 1 required keyword-only argument: 'keyword_required' print(1, 2, 3, keyword_required=4) # first positional arg: 1 # another positional arg: 2 # another positional arg: 3 # keyword_required value: 4 # keyword_only value: True

Section 110.5: Using **kwargs when calling functions You can use a dictionary to assign values to the function's parameters; using parameters name as keys in the dictionary and the value of these arguments bound to each key: def test_func(arg1, print("arg1: %s" print("arg2: %s" print("arg3: %s"

arg2, arg3): # Usual function with three arguments % arg1) % arg2) % arg3)

# Note that dictionaries are unordered, so we can switch arg2 and arg3. Only the names matter. kwargs = {"arg3": 3, "arg2": "two"} # Bind the first argument (ie. arg1) to 1, and use the kwargs dictionary to bind the others test_var_args_call(1, **kwargs)

Section 110.6: **kwargs and default values To use default values with **kwargs def fun(**kwargs): print kwargs.get('value', 0)

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fun() # print 0 fun(value=1) # print 1

Section 110.7: Using *args when calling functions The effect of using the * operator on an argument when calling a function is that of unpacking the list or a tuple argument def print_args(arg1, arg2): print(str(arg1) + str(arg2)) a = [1,2] b = tuple([3,4]) print_args(*a) # 12 print_args(*b) # 34

Note that the length of the starred argument need to be equal to the number of the function's arguments. A common python idiom is to use the unpacking operator * with the zip function to reverse its effects: a = [1,3,5,7,9] b = [2,4,6,8,10] zipped = zip(a,b) # [(1,2), (3,4), (5,6), (7,8), (9,10)] zip(*zipped) # (1,3,5,7,9), (2,4,6,8,10)

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Chapter 111: Garbage Collection Section 111.1: Reuse of primitive objects An interesting thing to note which may help optimize your applications is that primitives are actually also refcounted under the hood. Let's take a look at numbers; for all integers between -5 and 256, Python always reuses the same object: >>> >>> 797 >>> >>> >>> 799

import sys sys.getrefcount(1) a = 1 b = 1 sys.getrefcount(1)

Note that the refcount increases, meaning that a and b reference the same underlying object when they refer to the 1 primitive. However, for larger numbers, Python actually doesn't reuse the underlying object: >>> >>> 3 >>> >>> 3

a = 999999999 sys.getrefcount(999999999) b = 999999999 sys.getrefcount(999999999)

Because the refcount for 999999999 does not change when assigning it to a and b we can infer that they refer to two different underlying objects, even though they both are assigned the same primitive.

Section 111.2: Eects of the del command Removing a variable name from the scope using del v, or removing an object from a collection using del v[item] or del[i:j], or removing an attribute using del v.name, or any other way of removing references to an object, does not trigger any destructor calls or any memory being freed in and of itself. Objects are only destructed when their reference count reaches zero. >>> import gc >>> gc.disable() # disable garbage collector >>> class Track: def __init__(self): print("Initialized") def __del__(self): print("Destructed") >>> def bar(): return Track() >>> t = bar() Initialized >>> another_t = t # assign another reference >>> print("...") ... >>> del t # not destructed yet - another_t still refers to it >>> del another_t # final reference gone, object is destructed Destructed

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Section 111.3: Reference Counting The vast majority of Python memory management is handled with reference counting. Every time an object is referenced (e.g. assigned to a variable), its reference count is automatically increased. When it is dereferenced (e.g. variable goes out of scope), its reference count is automatically decreased. When the reference count reaches zero, the object is immediately destroyed and the memory is immediately freed. Thus for the majority of cases, the garbage collector is not even needed. >>> import gc; gc.disable() # disable garbage collector >>> class Track: def __init__(self): print("Initialized") def __del__(self): print("Destructed") >>> def foo(): Track() # destructed immediately since no longer has any references print("---") t = Track() # variable is referenced, so it's not destructed yet print("---") # variable is destructed when function exits >>> foo() Initialized Destructed --Initialized --Destructed

To demonstrate further the concept of references: >>> def bar(): return Track() >>> t = bar() Initialized >>> another_t = t # assign another reference >>> print("...") ... >>> t = None # not destructed yet - another_t still refers to it >>> another_t = None # final reference gone, object is destructed Destructed

Section 111.4: Garbage Collector for Reference Cycles The only time the garbage collector is needed is if you have a reference cycle. The simples example of a reference cycle is one in which A refers to B and B refers to A, while nothing else refers to either A or B. Neither A or B are accessible from anywhere in the program, so they can safely be destructed, yet their reference counts are 1 and so they cannot be freed by the reference counting algorithm alone. >>> import gc; gc.disable() # disable garbage collector >>> class Track: def __init__(self): print("Initialized") def __del__(self): print("Destructed")

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>>> A = Track() Initialized >>> B = Track() Initialized >>> A.other = B >>> B.other = A >>> del A; del B >>> gc.collect() Destructed Destructed 4

# objects are not destructed due to reference cycle # trigger collection

A reference cycle can be arbitrary long. If A points to B points to C points to ... points to Z which points to A, then neither A through Z will be collected, until the garbage collection phase: >>> objs = [Track() for _ in range(10)] Initialized Initialized Initialized Initialized Initialized Initialized Initialized Initialized Initialized Initialized >>> for i in range(len(objs)-1): ... objs[i].other = objs[i + 1] ... >>> objs[-1].other = objs[0] # complete the cycle >>> del objs # no one can refer to objs now - still not destructed >>> gc.collect() Destructed Destructed Destructed Destructed Destructed Destructed Destructed Destructed Destructed Destructed 20

Section 111.5: Forcefully deallocating objects You can force deallocate objects even if their refcount isn't 0 in both Python 2 and 3. Both versions use the ctypes module to do so. WARNING: doing this will leave your Python environment unstable and prone to crashing without a traceback! Using this method could also introduce security problems (quite unlikely) Only deallocate objects you're sure you'll never reference again. Ever. Python 3.x Version

≥ 3.0

import ctypes deallocated = 12345 ctypes.pythonapi._Py_Dealloc(ctypes.py_object(deallocated))

Python 2.x Version

≥ 2.3

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import ctypes, sys deallocated = 12345 (ctypes.c_char * sys.getsizeof(deallocated)).from_address(id(deallocated))[:4] = '\x00' * 4

After running, any reference to the now deallocated object will cause Python to either produce undefined behavior or crash - without a traceback. There was probably a reason why the garbage collector didn't remove that object... If you deallocate None, you get a special message - Fatal Python error: deallocating None before crashing.

Section 111.6: Viewing the refcount of an object >>> >>> >>> 2 >>> >>> 3 >>> >>> 2

import sys a = object() sys.getrefcount(a) b = a sys.getrefcount(a) del b sys.getrefcount(a)

Section 111.7: Do not wait for the garbage collection to clean up The fact that the garbage collection will clean up does not mean that you should wait for the garbage collection cycle to clean up. In particular you should not wait for garbage collection to close file handles, database connections and open network connections. for example: In the following code, you assume that the file will be closed on the next garbage collection cycle, if f was the last reference to the file. >>> f = open("test.txt") >>> del f

A more explicit way to clean up is to call f.close(). You can do it even more elegant, that is by using the with statement, also known as the context manager : >>> with open("test.txt") as f: ... pass ... # do something with f >>> #now the f object still exists, but it is closed

The with statement allows you to indent your code under the open file. This makes it explicit and easier to see how long a file is kept open. It also always closes a file, even if an exception is raised in the while block.

Section 111.8: Managing garbage collection There are two approaches for influencing when a memory cleanup is performed. They are influencing how often the automatic process is performed and the other is manually triggering a cleanup.

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The garbage collector can be manipulated by tuning the collection thresholds which affect the frequency at which the collector runs. Python uses a generation based memory management system. New objects are saved in the newest generation - generation0 and with each survived collection, objects are promoted to older generations. After reaching the last generation - generation2, they are no longer promoted. The thresholds can be changed using the following snippet: import gc gc.set_threshold(1000, 100, 10) # Values are just for demonstration purpose

The first argument represents the threshold for collecting generation0. Every time the number of allocations exceeds the number of deallocations by 1000 the garbage collector will be called. The older generations are not cleaned at each run to optimize the process. The second and third arguments are optional and control how frequently the older generations are cleaned. If generation0 was processed 100 times without cleaning generation1, then generation1 will be processed. Similarly, objects in generation2 will be processed only when the ones in generation1 were cleaned 10 times without touching generation2. One instance in which manually setting the thresholds is beneficial is when the program allocates a lot of small objects without deallocating them which leads to the garbage collector running too often (each generation0_threshold object allocations). Even though, the collector is pretty fast, when it runs on huge numbers of objects it poses a performance issue. Anyway, there's no one size fits all strategy for choosing the thresholds and it's use case dependable. Manually triggering a collection can be done as in the following snippet: import gc gc.collect()

The garbage collection is automatically triggered based on the number of allocations and deallocations, not on the consumed or available memory. Consequently, when working with big objects, the memory might get depleted before the automated cleanup is triggered. This makes a good use case for manually calling the garbage collector. Even though it's possible, it's not an encouraged practice. Avoiding memory leaks is the best option. Anyway, in big projects detecting the memory leak can be a tough task and manually triggering a garbage collection can be used as a quick solution until further debugging. For long-running programs, the garbage collection can be triggered on a time basis or on an event basis. An example for the first one is a web server that triggers a collection after a fixed number of requests. For the later, a web server that triggers a garbage collection when a certain type of request is received.

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Chapter 112: Pickle data serialisation Parameter Details object The object which is to be stored file

The open file which will contain the object

protocol

The protocol used for pickling the object (optional parameter)

buffer

A bytes object that contains a serialized object

Section 112.1: Using Pickle to serialize and deserialize an object The pickle module implements an algorithm for turning an arbitrary Python object into a series of bytes. This process is also called serializing the object. The byte stream representing the object can then be transmitted or stored, and later reconstructed to create a new object with the same characteristics. For the simplest code, we use the dump() and load() functions. To serialize the object import pickle # An arbitrary collection of objects supported by pickle. data = { 'a': [1, 2.0, 3, 4+6j], 'b': ("character string", b"byte string"), 'c': {None, True, False} } with open('data.pickle', 'wb') as f: # Pickle the 'data' dictionary using the highest protocol available. pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)

To deserialize the object import pickle with open('data.pickle', 'rb') as f: # The protocol version used is detected automatically, so we do not # have to specify it. data = pickle.load(f)

Using pickle and byte objects It is also possible to serialize into and deserialize out of byte objects, using the dumps and loads function, which are equivalent to dump and load. serialized_data = pickle.dumps(data, pickle.HIGHEST_PROTOCOL) # type(serialized_data) is bytes deserialized_data = pickle.loads(serialized_data) # deserialized_data == data

Section 112.2: Customize Pickled Data Some data cannot be pickled. Other data should not be pickled for other reasons. What will be pickled can be defined in __getstate__ method. This method must return something that is picklable. GoalKicker.com – Python® Notes for Professionals

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On the opposite side is __setstate__: it will receive what __getstate__ created and has to initialize the object. class A(object): def __init__(self, important_data): self.important_data = important_data # Add data which cannot be pickled: self.func = lambda: 7 # Add data which should never be pickled, because it expires quickly: self.is_up_to_date = False def __getstate__(self): return [self.important_data] # only this is needed def __setstate__(self, state): self.important_data = state[0] self.func = lambda: 7

# just some hard-coded unpicklable function

self.is_up_to_date = False

# even if it was before pickling

Now, this can be done: >>> a1 = A('very important') >>> >>> s = pickle.dumps(a1) # calls a1.__getstate__() >>> >>> a2 = pickle.loads(s) # calls a1.__setstate__(['very important']) >>> a2

>>> a2.important_data 'very important' >>> a2.func() 7

The implementation here pikles a list with one value: [self.important_data]. That was just an example, __getstate__ could have returned anything that is picklable, as long as __setstate__ knows how to do the

opposite. A good alternative is a dictionary of all values: {'important_data': self.important_data}. Constructor is not called! Note that in the previous example instance a2 was created in pickle.loads without ever calling A.__init__, so A.__setstate__ had to initialize everything that __init__ would have initialized if it were called.

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Chapter 113: Binary Data Section 113.1: Format a list of values into a byte object from struct import pack print(pack('I3c', 123, b'a', b'b', b'c'))

# b'{\x00\x00\x00abc'

Section 113.2: Unpack a byte object according to a format string from struct import unpack print(unpack('I3c', b'{\x00\x00\x00abc'))

# (123, b'a', b'b', b'c')

Section 113.3: Packing a structure The module "struct" provides facility to pack python objects as contiguous chunk of bytes or dissemble a chunk of bytes to python structures. The pack function takes a format string and one or more arguments, and returns a binary string. This looks very much like you are formatting a string except that the output is not a string but a chunk of bytes. import struct import sys print "Native byteorder: ", sys.byteorder # If no byteorder is specified, native byteorder is used buffer = struct.pack("ihb", 3, 4, 5) print "Byte chunk: ", repr(buffer) print "Byte chunk unpacked: ", struct.unpack("ihb", buffer) # Last element as unsigned short instead of unsigned char ( 2 Bytes) buffer = struct.pack("ihh", 3, 4, 5) print "Byte chunk: ", repr(buffer)

Output: Native byteorder: little Byte chunk: '\x03\x00\x00\x00\x04\x00\x05' Byte chunk unpacked: (3, 4, 5) Byte chunk: '\x03\x00\x00\x00\x04\x00\x05\x00' You could use network byte order with data received from network or pack data to send it to network. import struct # If no byteorder is specified, native byteorder is used buffer = struct.pack("hhh", 3, 4, 5) print "Byte chunk native byte order: ", repr(buffer) buffer = struct.pack("!hhh", 3, 4, 5) print "Byte chunk network byte order: ", repr(buffer)

Output: Byte chunk native byte order: '\x03\x00\x04\x00\x05\x00'

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Byte chunk network byte order: '\x00\x03\x00\x04\x00\x05' You can optimize by avoiding the overhead of allocating a new buffer by providing a buffer that was created earlier. import struct from ctypes import create_string_buffer bufferVar = create_string_buffer(8) bufferVar2 = create_string_buffer(8) # We use a buffer that has already been created # provide format, buffer, offset and data struct.pack_into("hhh", bufferVar, 0, 3, 4, 5) print "Byte chunk: ", repr(bufferVar.raw) struct.pack_into("hhh", bufferVar2, 2, 3, 4, 5) print "Byte chunk: ", repr(bufferVar2.raw)

Output: Byte chunk: '\x03\x00\x04\x00\x05\x00\x00\x00' Byte chunk: '\x00\x00\x03\x00\x04\x00\x05\x00'

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Chapter 114: Idioms Section 114.1: Dictionary key initializations Prefer dict.get method if you are not sure if the key is present. It allows you to return a default value if key is not found. The traditional method dict[key] would raise a KeyError exception. Rather than doing def add_student(): try: students['count'] += 1 except KeyError: students['count'] = 1

Do def add_student(): students['count'] = students.get('count', 0) + 1

Section 114.2: Switching variables To switch the value of two variables you can use tuple unpacking. x = True y = False x, y = y, x x # False y # True

Section 114.3: Use truth value testing Python will implicitly convert any object to a Boolean value for testing, so use it wherever possible. # Good examples, using implicit truth testing if attr: # do something if not attr: # do something # Bad examples, using specific types if attr == 1: # do something if attr == True: # do something if attr != '': # do something # If you are looking to specifically check for None, use 'is' or 'is not' if attr is None: # do something

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This generally produces more readable code, and is usually much safer when dealing with unexpected types. Click here for a list of what will be evaluated to False.

Section 114.4: Test for "__main__" to avoid unexpected code execution It is good practice to test the calling program's __name__ variable before executing your code. import sys def main(): # Your code starts here # Don't forget to provide a return code return 0 if __name__ == "__main__": sys.exit(main())

Using this pattern ensures that your code is only executed when you expect it to be; for example, when you run your file explicitly: python my_program.py

The benefit, however, comes if you decide to import your file in another program (for example if you are writing it as part of a library). You can then import your file, and the __main__ trap will ensure that no code is executed unexpectedly: # A new program file import my_program

# main() is not run

# But you can run main() explicitly if you really want it to run: my_program.main()

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VIDEO: Machine Learning A-Z: Hands-On Python In Data Science Learn to create Machine Learning Algorithms in Python from two Data Science experts. Code templates included.

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Chapter 115: Data Serialization Parameter protocol

Details Using pickle or cPickle, it is the method that objects are being Serialized/Unserialized. You probably want to use pickle.HIGHEST_PROTOCOL here, which means the newest method.

Section 115.1: Serialization using JSON JSON is a cross language, widely used method to serialize data Supported data types : int, float, boolean, string, list and dict. See -> JSON Wiki for more Here is an example demonstrating the basic usage of JSON: import json families = (['John'], ['Mark', 'David', {'name': 'Avraham'}]) # Dumping it into string json_families = json.dumps(families) # [["John"], ["Mark", "David", {"name": "Avraham"}]] # Dumping it to file with open('families.json', 'w') as json_file: json.dump(families, json_file) # Loading it from string json_families = json.loads(json_families) # Loading it from file with open('families.json', 'r') as json_file: json_families = json.load(json_file)

See JSON-Module for detailed information about JSON.

Section 115.2: Serialization using Pickle Here is an example demonstrating the basic usage of pickle: # Importing pickle try: import cPickle as pickle # Python 2 except ImportError: import pickle # Python 3 # Creating Pythonic object: class Family(object): def __init__(self, names): self.sons = names def __str__(self): return ' '.join(self.sons) my_family = Family(['John', 'David']) # Dumping to string pickle_data = pickle.dumps(my_family, pickle.HIGHEST_PROTOCOL)

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# Dumping to file with open('family.p', 'w') as pickle_file: pickle.dump(families, pickle_file, pickle.HIGHEST_PROTOCOL) # Loading from string my_family = pickle.loads(pickle_data) # Loading from file with open('family.p', 'r') as pickle_file: my_family = pickle.load(pickle_file)

See Pickle for detailed information about Pickle. WARNING: The official documentation for pickle makes it clear that there are no security guarantees. Don't load any data you don't trust its origin.

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Chapter 116: Multiprocessing Section 116.1: Running Two Simple Processes A simple example of using multiple processes would be two processes (workers) that are executed separately. In the following example, two processes are started: countUp() counts 1 up, every second. countDown() counts 1 down, every second. import multiprocessing import time from random import randint def countUp(): i = 0 while i = 0: print('Down:\t{}'.format(i)) time.sleep(randint(1, 3)) # sleep 1, 2 or 3 seconds i -= 1 if __name__ == # Initiate workerUp = workerDown

'__main__': the workers. multiprocessing.Process(target=countUp) = multiprocessing.Process(target=countDown)

# Start the workers. workerUp.start() workerDown.start() # Join the workers. This will block in the main (parent) process # until the workers are complete. workerUp.join() workerDown.join()

The output is as follows: Up: 0 Down: Up: 1 Up: 2 Down: Up: 3 Down: Down:

3

2 1 0

Section 116.2: Using Pool and Map from multiprocessing import Pool

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def cube(x): return x ** 3 if __name__ == "__main__": pool = Pool(5) result = pool.map(cube, [0, 1, 2, 3]) Pool is a class which manages multiple Workers (processes) behind the scenes and lets you, the programmer, use. Pool(5) creates a new Pool with 5 processes, and pool.map works just like map but it uses multiple processes (the

amount defined when creating the pool). Similar results can be achieved using map_async, apply and apply_async which can be found in the documentation.

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Chapter 117: Multithreading Threads allow Python programs to handle multiple functions at once as opposed to running a sequence of commands individually. This topic explains the principles behind threading and demonstrates its usage.

Section 117.1: Basics of multithreading Using the threading module, a new thread of execution may be started by creating a new threading.Thread and assigning it a function to execute: import threading def foo(): print "Hello threading!" my_thread = threading.Thread(target=foo)

The target parameter references the function (or callable object) to be run. The thread will not begin execution until start is called on the Thread object. Starting a Thread my_thread.start() # prints 'Hello threading!'

Now that my_thread has run and terminated, calling start again will produce a RuntimeError. If you'd like to run your thread as a daemon, passing the daemon=True kwarg, or setting my_thread.daemon to True before calling start(), causes your Thread to run silently in the background as a daemon.

Joining a Thread In cases where you split up one big job into several small ones and want to run them concurrently, but need to wait for all of them to finish before continuing, Thread.join() is the method you're looking for. For example, let's say you want to download several pages of a website and compile them into a single page. You'd do this: import requests from threading import Thread from queue import Queue q = Queue(maxsize=20) def put_page_to_q(page_num): q.put(requests.get('http://some-website.com/page_%s.html' % page_num) def compile(q): # magic function that needs all pages before being able to be executed if not q.full(): raise ValueError else: print("Done compiling!") threads = [] for page_num in range(20): t = Thread(target=requests.get, args=(page_num,)) t.start() threads.append(t)

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# Next, join all threads to make sure all threads are done running before # we continue. join() is a blocking call (unless specified otherwise using # the kwarg blocking=False when calling join) for t in threads: t.join() # Call compile() now, since all threads have completed compile(q)

A closer look at how join() works can be found here. Create a Custom Thread Class Using threading.Thread class we can subclass new custom Thread class. we must override run method in a subclass. from threading import Thread import time class Sleepy(Thread): def run(self): time.sleep(5) print("Hello form Thread") if __name__ == "__main__": t = Sleepy() t.start() # start method automatic call Thread class run method. # print 'The main program continues to run in foreground.' t.join() print("The main program continues to run in the foreground.")

Section 117.2: Communicating between threads There are multiple threads in your code and you need to safely communicate between them. You can use a Queue from the queue library. from queue import Queue from threading import Thread # create a data producer def producer(output_queue): while True: data = data_computation() output_queue.put(data) # create a consumer def consumer(input_queue): while True: # retrieve data (blocking) data = input_queue.get() # do something with the data # indicate data has been consumed input_queue.task_done()

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Creating producer and consumer threads with a shared queue q = Queue() t1 = Thread(target=consumer, args=(q,)) t2 = Thread(target=producer, args=(q,)) t1.start() t2.start()

Section 117.3: Creating a worker pool Using threading & queue: from socket import socket, AF_INET, SOCK_STREAM from threading import Thread from queue import Queue def echo_server(addr, nworkers): print('Echo server running at', addr) # Launch the client workers q = Queue() for n in range(nworkers): t = Thread(target=echo_client, args=(q,)) t.daemon = True t.start() # Run the server sock = socket(AF_INET, SOCK_STREAM) sock.bind(addr) sock.listen(5) while True: client_sock, client_addr = sock.accept() q.put((client_sock, client_addr)) echo_server(('',15000), 128)

Using concurrent.futures.Threadpoolexecutor: from socket import AF_INET, SOCK_STREAM, socket from concurrent.futures import ThreadPoolExecutor def echo_server(addr): print('Echo server running at', addr) pool = ThreadPoolExecutor(128) sock = socket(AF_INET, SOCK_STREAM) sock.bind(addr) sock.listen(5) while True: client_sock, client_addr = sock.accept() pool.submit(echo_client, client_sock, client_addr) echo_server(('',15000))

Python Cookbook, 3rd edition, by David Beazley and Brian K. Jones (O’Reilly). Copyright 2013 David Beazley and Brian Jones, 978-1-449-34037-7.

Section 117.4: Advanced use of multithreads This section will contain some of the most advanced examples realized using Multithreading. GoalKicker.com – Python® Notes for Professionals

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Advanced printer (logger) A thread that prints everything is received and modifies the output according to the terminal width. The nice part is that also the "already written" output is modified when the width of the terminal changes. #!/usr/bin/env python2 import threading import Queue import time import sys import subprocess from backports.shutil_get_terminal_size import get_terminal_size printq = Queue.Queue() interrupt = False lines = [] def main(): ptt = threading.Thread(target=printer) # Turn the printer on ptt.daemon = True ptt.start() # Stupid example of stuff to print for i in xrange(1,100): printq.put(' '.join([str(x) for x in range(1,i)])) to the printer time.sleep(.5)

# The actual way to send stuff

def split_line(line, cols): if len(line) > cols: new_line = '' ww = line.split() i = 0 while len(new_line) 0: print("Count value", count) count -= 1 return if __name__ == "__main__": p1 = multiprocessing.Process(target=countdown, args=(10,)) p1.start() p2 = multiprocessing.Process(target=countdown, args=(20,)) p2.start() p1.join() p2.join()

Here, each function is executed in a new process. Since a new instance of Python VM is running the code, there is no GIL and you get parallelism running on multiple cores. The Process.start method launches this new process and run the function passed in the target argument with the arguments args. The Process.join method waits for the end of the execution of processes p1 and p2. The new processes are launched differently depending on the version of python and the platform on which the code is running e.g.: Windows uses spawn to create the new process. With unix systems and version earlier than 3.3, the processes are created using a fork. Note that this method does not respect the POSIX usage of fork and thus leads to unexpected behaviors, especially when interacting with other multiprocessing libraries. With unix system and version 3.4+, you can choose to start the new processes with either fork, forkserver or spawn using multiprocessing.set_start_method at the beginning of your program. forkserver and spawn methods are slower than forking but avoid some unexpected behaviors.

POSIX fork usage: After a fork in a multithreaded program, the child can safely call only async-signal-safe functions until such time as it calls execve. (see) Using fork, a new process will be launched with the exact same state for all the current mutex but only the MainThread will be launched. This is unsafe as it could lead to race conditions e.g.:

If you use a Lock in MainThread and pass it to another thread which is supposed to lock it at some point. If the fork occurs simultaneously, the new process will start with a locked lock which will never be released as the second thread does not exist in this new process.

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Actually, this kind of behavior should not occurred in pure python as multiprocessing handles it properly but if you are interacting with other library, this kind of behavior can occurs, leading to crash of your system (for instance with numpy/accelerated on macOS).

Section 119.2: The threading module from __future__ import print_function import threading def counter(count): while count > 0: print("Count value", count) count -= 1 return t1 = threading.Thread(target=countdown,args=(10,)) t1.start() t2 = threading.Thread(target=countdown,args=(20,)) t2.start()

In certain implementations of Python such as CPython, true parallelism is not achieved using threads because of using what is known as the GIL, or Global Interpreter Lock. Here is an excellent overview of Python concurrency: Python concurrency by David Beazley (YouTube)

Section 119.3: Passing data between multiprocessing processes Because data is sensitive when dealt with between two threads (think concurrent read and concurrent write can conflict with one another, causing race conditions), a set of unique objects were made in order to facilitate the passing of data back and forth between threads. Any truly atomic operation can be used between threads, but it is always safe to stick with Queue. import multiprocessing import queue my_Queue=multiprocessing.Queue() #Creates a queue with an undefined maximum size #this can be dangerous as the queue becomes increasingly large #it will take a long time to copy data to/from each read/write thread

Most people will suggest that when using queue, to always place the queue data in a try: except: block instead of using empty. However, for applications where it does not matter if you skip a scan cycle (data can be placed in the queue while it is flipping states from queue.Empty==True to queue.Empty==False) it is usually better to place read and write access in what I call an Iftry block, because an 'if' statement is technically more performant than catching the exception. import multiprocessing import queue '''Import necessary Python standard libraries, multiprocessing for classes and queue for the queue exceptions it provides''' def Queue_Iftry_Get(get_queue, default=None, use_default=False, func=None, use_func=False): '''This global method for the Iftry block is provided for its reuse and standard functionality, the if also saves on performance as opposed to catching the exception, which is expensive. It also allows the user to specify a function for the outgoing data to use,

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and a default value to return if the function cannot return the value from the queue''' if get_queue.empty(): if use_default: return default else: try: value = get_queue.get_nowait() except queue.Empty: if use_default: return default else: if use_func: return func(value) else: return value def Queue_Iftry_Put(put_queue, value): '''This global method for the Iftry block is provided because of its reuse and standard functionality, the If also saves on performance as opposed to catching the exception, which is expensive. Return True if placing value in the queue was successful. Otherwise, false''' if put_queue.full(): return False else: try: put_queue.put_nowait(value) except queue.Full: return False else: return True

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Chapter 120: Parallel computation Section 120.1: Using the multiprocessing module to parallelise tasks import multiprocessing def fib(n): """computing the Fibonacci in an inefficient way was chosen to slow down the CPU.""" if n value descr.__set__(self, obj, value) --> None descr.__delete__(self, obj) --> None

An implemented example: class DescPrinter(object): """A data descriptor that logs activity.""" _val = 7 def __get__(self, obj, objtype=None): print('Getting ...') return self._val def __set__(self, obj, val): print('Setting', val) self._val = val def __delete__(self, obj): print('Deleting ...') del self._val

class Foo(): x = DescPrinter() i = Foo() i.x # Getting ... # 7 i.x = 100 # Setting 100 i.x # Getting ... # 100 del i.x # Deleting ... i.x # Getting ... # 7

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Section 128.2: Two-way conversions Descriptor objects can allow related object attributes to react to changes automatically. Suppose we want to model an oscillator with a given frequency (in Hertz) and period (in seconds). When we update the frequency we want the period to update, and when we update the period we want the frequency to update: >>> oscillator = Oscillator(freq=100.0) # Set frequency to 100.0 Hz >>> oscillator.period # Period is 1 / frequency, i.e. 0.01 seconds 0.01 >>> oscillator.period = 0.02 # Set period to 0.02 seconds >>> oscillator.freq # The frequency is automatically adjusted 50.0 >>> oscillator.freq = 200.0 # Set the frequency to 200.0 Hz >>> oscillator.period # The period is automatically adjusted 0.005

We pick one of the values (frequency, in Hertz) as the "anchor," i.e. the one that can be set with no conversion, and write a descriptor class for it: class Hertz(object): def __get__(self, instance, owner): return self.value def __set__(self, instance, value): self.value = float(value)

The "other" value (period, in seconds) is defined in terms of the anchor. We write a descriptor class that does our conversions: class Second(object): def __get__(self, instance, owner): # When reading period, convert from frequency return 1 / instance.freq def __set__(self, instance, value): # When setting period, update the frequency instance.freq = 1 / float(value)

Now we can write the Oscillator class: class Oscillator(object): period = Second() # Set the other value as a class attribute def __init__(self, freq): self.freq = Hertz() # Set the anchor value as an instance attribute self.freq = freq # Assign the passed value - self.period will be adjusted

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Chapter 129: tempfile NamedTemporaryFile param description mode mode to open file, default=w+b delete

To delete file on closure, default=True

suffix

filename suffix, default=''

prefix

filename prefix, default='tmp'

dir

dirname to place tempfile, default=None

buffsize default=-1, (operating system default used)

Section 129.1: Create (and write to a) known, persistent temporary file You can create temporary files which has a visible name on the file system which can be accessed via the name property. The file can, on unix systems, be configured to delete on closure (set by delete param, default is True) or can be reopened later. The following will create and open a named temporary file and write 'Hello World!' to that file. The filepath of the temporary file can be accessed via name, in this example it is saved to the variable path and printed for the user. The file is then re-opened after closing the file and the contents of the tempfile are read and printed for the user. import tempfile with tempfile.NamedTemporaryFile(delete=False) as t: t.write('Hello World!') path = t.name print path with open(path) as t: print t.read()

Output: /tmp/tmp6pireJ Hello World!

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VIDEO: Python for Data Science and Machine Learning Bootcamp Learn how to use NumPy, Pandas, Seaborn, Matplotlib , Plotly, Scikit-Learn , Machine Learning, Tensorflow, and more!

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Chapter 130: Input, Subset and Output External Data Files using Pandas This section shows basic code for reading, sub-setting and writing external data files using pandas.

Section 130.1: Basic Code to Import, Subset and Write External Data Files Using Pandas # Print the working directory import os print os.getcwd() # C:\Python27\Scripts # Set the working directory os.chdir('C:/Users/general1/Documents/simple Python files') print os.getcwd() # C:\Users\general1\Documents\simple Python files # load pandas import pandas as pd # read a csv data file named 'small_dataset.csv' containing 4 lines and 3 variables my_data = pd.read_csv("small_dataset.csv") my_data # x y z # 0 1 2 3 # 1 4 5 6 # 2 7 8 9 # 3 10 11 12 my_data.shape # (4, 3)

# number of rows and columns in data set

my_data.shape[0] # 4

# number of rows in data set

my_data.shape[1] # 3

# number of columns in data set

# Python uses 0-based indexing. The first row or column in a data set is located # at position 0. In R the first row or column in a data set is located # at position 1. # Select the my_data[0:2] # x y #0 1 2 #1 4 5

first two rows z 3 6

# Select the second and third rows my_data[1:3] # x y z # 1 4 5 6 # 2 7 8 9 # Select the third row my_data[2:3] # x y z #2 7 8 9

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# Select the first two elements of the first column my_data.iloc[0:2, 0:1] # x # 0 1 # 1 4 # Select the first element of the variables y and z my_data.loc[0, ['y', 'z']] # y 2 # z 3 # Select the first three elements of the variables y and z my_data.loc[0:2, ['y', 'z']] # y z # 0 2 3 # 1 5 6 # 2 8 9 # Write the first three elements of the variables y and z # to an external file. Here index = 0 means do not write row names. my_data2 = my_data.loc[0:2, ['y', 'z']] my_data2.to_csv('my.output.csv', index = 0)

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Chapter 131: Unzipping Files To extract or uncompress a tarball, ZIP, or gzip file, Python's tarfile, zipfile, and gzip modules are provided respectively. Python's tarfile module provides the TarFile.extractall(path=".", members=None) function for extracting from a tarball file. Python's zipfile module provides the ZipFile.extractall([path[, members[, pwd]]]) function for extracting or unzipping ZIP compressed files. Finally, Python's gzip module provides the

GzipFile class for decompressing.

Section 131.1: Using Python ZipFile.extractall() to decompress a ZIP file file_unzip = 'filename.zip' unzip = zipfile.ZipFile(file_unzip, 'r') unzip.extractall() unzip.close()

Section 131.2: Using Python TarFile.extractall() to decompress a tarball file_untar = 'filename.tar.gz' untar = tarfile.TarFile(file_untar) untar.extractall() untar.close()

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Chapter 132: Working with ZIP archives Section 132.1: Examining Zipfile Contents There are a few ways to inspect the contents of a zipfile. You can use the printdir to just get a variety of information sent to stdout with zipfile.ZipFile(filename) as zip: zip.printdir() # # # # # # #

Out: File Name pyexpat.pyd python.exe python3.dll python35.dll etc.

Modified 2016-06-25 22:13:34 2016-06-25 22:13:34 2016-06-25 22:13:34 2016-06-25 22:13:34

Size 157336 39576 51864 3127960

We can also get a list of filenames with the namelist method. Here, we simply print the list: with zipfile.ZipFile(filename) as zip: print(zip.namelist()) # Out: ['pyexpat.pyd', 'python.exe', 'python3.dll', 'python35.dll', ... etc. ...]

Instead of namelist, we can call the infolist method, which returns a list of ZipInfo objects, which contain additional information about each file, for instance a timestamp and file size: with zipfile.ZipFile(filename) as zip: info = zip.infolist() print(zip[0].filename) print(zip[0].date_time) print(info[0].file_size) # Out: pyexpat.pyd # Out: (2016, 6, 25, 22, 13, 34) # Out: 157336

Section 132.2: Opening Zip Files To start, import the zipfile module, and set the filename. import zipfile filename = 'zipfile.zip'

Working with zip archives is very similar to working with files, you create the object by opening the zipfile, which lets you work on it before closing the file up again. zip = zipfile.ZipFile(filename) print(zip) # zip.close()

In Python 2.7 and in Python 3 versions higher than 3.2, we can use the with context manager. We open the file in "read" mode, and then print a list of filenames: GoalKicker.com – Python® Notes for Professionals

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with zipfile.ZipFile(filename, 'r') as z: print(zip) #

Section 132.3: Extracting zip file contents to a directory Extract all file contents of a zip file import zipfile with zipfile.ZipFile('zipfile.zip','r') as zfile: zfile.extractall('path')

If you want extract single files use extract method, it takes name list and path as input parameter import zipfile f=open('zipfile.zip','rb') zfile=zipfile.ZipFile(f) for cont in zfile.namelist(): zfile.extract(cont,path)

Section 132.4: Creating new archives To create new archive open zipfile with write mode. import zipfile new_arch=zipfile.ZipFile("filename.zip",mode="w")

To add files to this archive use write() method. new_arch.write('filename.txt','filename_in_archive.txt') #first parameter is filename and second parameter is filename in archive by default filename will be taken if not provided new_arch.close()

If you want to write string of bytes into the archive you can use writestr() method. str_bytes="string buffer" new_arch.writestr('filename_string_in_archive.txt',str_bytes) new_arch.close()

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Chapter 133: Getting start with GZip This module provides a simple interface to compress and decompress files just like the GNU programs gzip and gunzip would. The data compression is provided by the zlib module. The gzip module provides the GzipFile class which is modeled after Python’s File Object. The GzipFile class reads and writes gzip-format files, automatically compressing or decompressing the data so that it looks like an ordinary file object.

Section 133.1: Read and write GNU zip files import gzip import os outfilename = 'example.txt.gz' output = gzip.open(outfilename, 'wb') try: output.write('Contents of the example file go here.\n') finally: output.close() print outfilename, 'contains', os.stat(outfilename).st_size, 'bytes of compressed data' os.system('file -b --mime %s' % outfilename)

Save it as 1gzip_write.py1.Run it through terminal. $ python gzip_write.py application/x-gzip; charset=binary example.txt.gz contains 68 bytes of compressed data

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Chapter 134: Stack A stack is a container of objects that are inserted and removed according to the last-in first-out (LIFO) principle. In the pushdown stacks only two operations are allowed: push the item into the stack, and pop the item out of the stack. A stack is a limited access data structure - elements can be added and removed from the stack only at the top. Here is a structural definition of a Stack: a stack is either empty or it consists of a top and the rest which is a Stack.

Section 134.1: Creating a Stack class with a List Object Using a list object you can create a fully functional generic Stack with helper methods such as peeking and checking if the stack is Empty. Check out the official python docs for using list as Stack here. #define a stack class class Stack: def __init__(self): self.items = [] #method to check the stack is empty or not def isEmpty(self): return self.items == [] #method for pushing an item def push(self, item): self.items.append(item) #method for popping an item def pop(self): return self.items.pop() #check what item is on top of the stack without removing it def peek(self): return self.items[-1] #method to get the size def size(self): return len(self.items) #to view the entire stack def fullStack(self): return self.items

An example run: stack = Stack() print('Current stack:', stack.fullStack()) print('Stack empty?:', stack.isEmpty()) print('Pushing integer 1') stack.push(1) print('Pushing string "Told you, I am generic stack!"') stack.push('Told you, I am generic stack!') print('Pushing integer 3') stack.push(3) print('Current stack:', stack.fullStack()) print('Popped item:', stack.pop()) print('Current stack:', stack.fullStack()) print('Stack empty?:', stack.isEmpty())

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Output: Current stack: [] Stack empty?: True Pushing integer 1 Pushing string "Told you, I am generic stack!" Pushing integer 3 Current stack: [1, 'Told you, I am generic stack!', 3] Popped item: 3 Current stack: [1, 'Told you, I am generic stack!'] Stack empty?: False

Section 134.2: Parsing Parentheses Stacks are often used for parsing. A simple parsing task is to check whether a string of parentheses are matching. For example, the string ([]) is matching, because the outer and inner brackets form pairs. ()) is not matching, because the last ) has no partner. ([)] is also not matching, because pairs must be either entirely inside or outside other pairs. def checkParenth(str): stack = Stack() pushChars, popChars = ")}]" for c in str: if c in pushChars: stack.push(c) elif c in popChars: if stack.isEmpty(): return False else: stackTop = stack.pop() # Checks to see whether the opening bracket matches the closing one balancingBracket = pushChars[popChars.index(c)] if stackTop != balancingBracket: return False else: return False return not stack.isEmpty()

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VIDEO: Machine Learning A-Z: Hands-On Python In Data Science Learn to create Machine Learning Algorithms in Python from two Data Science experts. Code templates included.

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Chapter 135: Working around the Global Interpreter Lock (GIL) Section 135.1: Multiprocessing.Pool The simple answer, when asking how to use threads in Python is: "Don't. Use processes, instead." The multiprocessing module lets you create processes with similar syntax to creating threads, but I prefer using their convenient Pool object. Using the code that David Beazley first used to show the dangers of threads against the GIL, we'll rewrite it using multiprocessing.Pool: David Beazley's code that showed GIL threading problems from threading import Thread import time def countdown(n): while n > 0: n -= 1 COUNT = 10000000 t1 = Thread(target=countdown,args=(COUNT/2,)) t2 = Thread(target=countdown,args=(COUNT/2,)) start = time.time() t1.start();t2.start() t1.join();t2.join() end = time.time() print end-start

Re-written using multiprocessing.Pool: import multiprocessing import time def countdown(n): while n > 0: n -= 1 COUNT = 10000000 start = time.time() with multiprocessing.Pool as pool: pool.map(countdown, [COUNT/2, COUNT/2]) pool.close() pool.join() end = time.time() print(end-start)

Instead of creating threads, this creates new processes. Since each process is its own interpreter, there are no GIL collisions. multiprocessing.Pool will open as many processes as there are cores on the machine, though in the example above, it would only need two. In a real-world scenario, you want to design your list to have at least as much length as there are processors on your machine. The Pool will run the function you tell it to run with each argument, up to the number of processes it creates. When the function finishes, any remaining functions in the list will be run on that process. I've found that, even using the with statement, if you don't close and join the pool, the processes continue to exist. GoalKicker.com – Python® Notes for Professionals

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To clean up resources, I always close and join my pools.

Section 135.2: Cython nogil: Cython is an alternative python interpreter. It uses the GIL, but lets you disable it. See their documentation As an example, using the code that David Beazley first used to show the dangers of threads against the GIL, we'll rewrite it using nogil: David Beazley's code that showed GIL threading problems from threading import Thread import time def countdown(n): while n > 0: n -= 1 COUNT = 10000000 t1 = Thread(target=countdown,args=(COUNT/2,)) t2 = Thread(target=countdown,args=(COUNT/2,)) start = time.time() t1.start();t2.start() t1.join();t2.join() end = time.time() print end-start

Re-written using nogil (ONLY WORKS IN CYTHON): from threading import Thread import time def countdown(n): while n > 0: n -= 1 COUNT = 10000000 with nogil: t1 = Thread(target=countdown,args=(COUNT/2,)) t2 = Thread(target=countdown,args=(COUNT/2,)) start = time.time() t1.start();t2.start() t1.join();t2.join() end = time.time() print end-start

It's that simple, as long as you're using cython. Note that the documentation says you must make sure not to change any python objects: Code in the body of the statement must not manipulate Python objects in any way, and must not call anything that manipulates Python objects without first re-acquiring the GIL. Cython currently does not check this.

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Chapter 136: Deployment Section 136.1: Uploading a Conda Package Before starting you must have: Anaconda installed on your system Account on Binstar If you are not using Anaconda 1.6+ install the binstar command line client: $ conda install binstar $ conda update binstar

If you are not using Anaconda the Binstar is also available on pypi: $ pip install binstar

Now we can login: $ binstar login

Test your login with the whoami command: $ binstar whoami

We are going to be uploading a package with a simple ‘hello world’ function. To follow along start by getting my demonstration package repo from Github: $ git clone https://github.com//

This a small directory that looks like this: package/ setup.py test_package/ __init__.py hello.py bld.bat build.sh meta.yaml Setup.py is the standard python build file and hello.py has our single hello_world() function.

The bld.bat, build.sh, and meta.yaml are scripts and metadata for the Conda package. You can read the Conda build page for more info on those three files and their purpose. Now we create the package by running: $ conda build test_package/

That is all it takes to create a Conda package. The final step is uploading to binstar by copying and pasting the last line of the print out after running the conda build test_package/ command. On my system the command is:

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$ binstar upload /home/xavier/anaconda/conda-bld/linux-64/test_package-0.1.0-py27_0.tar.bz2

Since it is your first time creating a package and release you will be prompted to fill out some text fields which could alternatively be done through the web app. You will see a done printed out to confirm you have successfully uploaded your Conda package to Binstar.

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Chapter 137: Logging Section 137.1: Introduction to Python Logging This module defines functions and classes which implement a flexible event logging system for applications and libraries. The key benefit of having the logging API provided by a standard library module is that all Python modules can participate in logging, so your application log can include your own messages integrated with messages from thirdparty modules. So, let's start: Example Configuration Directly in Code import logging logger = logging.getLogger() handler = logging.StreamHandler() formatter = logging.Formatter( '%(asctime)s %(name)-12s %(levelname)-8s %(message)s') handler.setFormatter(formatter) logger.addHandler(handler) logger.setLevel(logging.DEBUG) logger.debug('this is a %s test', 'debug')

Output example: 2016-07-26 18:53:55,332 root DEBUG this is a debug test

Example Configuration via an INI File Assuming the file is named logging_config.ini. More details for the file format are in the logging configuration section of the logging tutorial. [loggers] keys=root [handlers] keys=stream_handler [formatters] keys=formatter [logger_root] level=DEBUG handlers=stream_handler [handler_stream_handler] class=StreamHandler level=DEBUG formatter=formatter args=(sys.stderr,) [formatter_formatter]

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format=%(asctime)s %(name)-12s %(levelname)-8s %(message)s

Then use logging.config.fileConfig() in the code: import logging from logging.config import fileConfig fileConfig('logging_config.ini') logger = logging.getLogger() logger.debug('often makes a very good meal of %s', 'visiting tourists')

Example Configuration via a Dictionary As of Python 2.7, you can use a dictionary with configuration details. PEP 391 contains a list of the mandatory and optional elements in the configuration dictionary. import logging from logging.config import dictConfig logging_config = dict( version = 1, formatters = { 'f': {'format': '%(asctime)s %(name)-12s %(levelname)-8s %(message)s'} }, handlers = { 'h': {'class': 'logging.StreamHandler', 'formatter': 'f', 'level': logging.DEBUG} }, root = { 'handlers': ['h'], 'level': logging.DEBUG, }, ) dictConfig(logging_config) logger = logging.getLogger() logger.debug('often makes a very good meal of %s', 'visiting tourists')

Section 137.2: Logging exceptions If you want to log exceptions you can and should make use of the logging.exception(msg) method: >>> import logging >>> logging.basicConfig() >>> try: ... raise Exception('foo') ... except: ... logging.exception('bar') ... ERROR:root:bar Traceback (most recent call last): File "", line 2, in Exception: foo

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As logging.exception(msg) expects a msg arg, it is a common pitfall to pass the exception into the logging call like this: >>> try: ... raise Exception('foo') ... except Exception as e: ... logging.exception(e) ... ERROR:root:foo Traceback (most recent call last): File "", line 2, in Exception: foo

While it might look as if this is the right thing to do at first, it is actually problematic due to the reason how exceptions and various encoding work together in the logging module: >>> try: ... raise Exception(u'föö') ... except Exception as e: ... logging.exception(e) ... Traceback (most recent call last): File "/.../python2.7/logging/__init__.py", line 861, in msg = self.format(record) File "/.../python2.7/logging/__init__.py", line 734, in return fmt.format(record) File "/.../python2.7/logging/__init__.py", line 469, in s = self._fmt % record.__dict__ UnicodeEncodeError: 'ascii' codec can't encode characters range(128) Logged from file , line 4

emit format format in position 1-2: ordinal not in

Trying to log an exception that contains unicode chars, this way will fail miserably. It will hide the stacktrace of the original exception by overriding it with a new one that is raised during formatting of your logging.exception(e) call. Obviously, in your own code, you might be aware of the encoding in exceptions. However, 3rd party libs might handle this in a different way. Correct Usage: If instead of the exception you just pass a message and let python do its magic, it will work: >>> try: ... raise Exception(u'föö') ... except Exception as e: ... logging.exception('bar') ... ERROR:root:bar Traceback (most recent call last): File "", line 2, in Exception: f\xf6\xf6

As you can see we don't actually use e in that case, the call to logging.exception(...) magically formats the most recent exception. Logging exceptions with non ERROR log levels

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If you want to log an exception with another log level than ERROR, you can use the exc_info argument of the default loggers: logging.debug('exception occurred', exc_info=1) logging.info('exception occurred', exc_info=1) logging.warning('exception occurred', exc_info=1)

Accessing the exception's message Be aware that libraries out there might throw exceptions with messages as any of unicode or (utf-8 if you're lucky) byte-strings. If you really need to access an exception's text, the only reliable way, that will always work, is to use repr(e) or the %r string formatting: >>> try: ... raise Exception(u'föö') ... except Exception as e: ... logging.exception('received this exception: %r' % e) ... ERROR:root:received this exception: Exception(u'f\xf6\xf6',) Traceback (most recent call last): File "", line 2, in Exception: f\xf6\xf6

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Chapter 138: Web Server Gateway Interface (WSGI) Parameter Details start_response A function used to process the start

Section 138.1: Server Object (Method) Our server object is given an 'application' parameter which can be any callable application object (see other examples). It writes first the headers, then the body of data returned by our application to the system standard output. import os, sys def run(application): environ['wsgi.input'] environ['wsgi.errors']

= sys.stdin = sys.stderr

headers_set = [] headers_sent = [] def write (data): """ Writes header data from 'start_response()' as well as body data from 'response' to system standard output. """ if not headers_set: raise AssertionError("write() before start_response()") elif not headers_sent: status, response_headers = headers_sent[:] = headers_set sys.stdout.write('Status: %s\r\n' % status) for header in response_headers: sys.stdout.write('%s: %s\r\n' % header) sys.stdout.write('\r\n') sys.stdout.write(data) sys.stdout.flush() def start_response(status, response_headers): """ Sets headers for the response returned by this server.""" if headers_set: raise AssertionError("Headers already set!") headers_set[:] = [status, response_headers] return write # This is the most important piece of the 'server object' # Our result will be generated by the 'application' given to this method as a parameter result = application(environ, start_response) try: for data in result: if data: write(data) # Body isn't empty send its data to 'write()' if not headers_sent: write('') # Body is empty, send empty string to 'write()'

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Chapter 139: Python Server Sent Events Server Sent Events (SSE) is a unidirectional connection between a server and a client (usually a web browser) that allows the server to "push" information to the client. It is much like websockets and long polling. The main difference between SSE and websockets is that SSE is unidirectional, only the server can send info to the client, where as with websockets, both can send info to each other. SSE is typically considered to be much simpler to use/implement than websockets.

Section 139.1: Flask SSE @route("/stream") def stream(): def event_stream(): while True: if message_to_send: yield "data: {}\n\n".format(message_to_send)" return Response(event_stream(), mimetype="text/event-stream")

Section 139.2: Asyncio SSE This example uses the asyncio SSE library: https://github.com/brutasse/asyncio-sse import asyncio import sse class Handler(sse.Handler): @asyncio.coroutine def handle_request(self): yield from asyncio.sleep(2) self.send('foo') yield from asyncio.sleep(2) self.send('bar', event='wakeup') start_server = sse.serve(Handler, 'localhost', 8888) asyncio.get_event_loop().run_until_complete(start_server) asyncio.get_event_loop().run_forever()

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Chapter 140: Alternatives to switch statement from other languages Section 140.1: Use what the language oers: the if/else construct Well, if you want a switch/case construct, the most straightforward way to go is to use the good old if/else construct: def switch(value): if value == 1: return "one" if value == 2: return "two" if value == 42: return "the answer to the question about life, the universe and everything" raise Exception("No case found!")

it might look redundant, and not always pretty, but that's by far the most efficient way to go, and it does the job: >>> switch(1) one >>> switch(2) two >>> switch(3) … Exception: No case found! >>> switch(42) the answer to the question about life the universe and everything

Section 140.2: Use a dict of functions Another straightforward way to go is to create a dictionary of functions: switch = { 1: lambda: 'one', 2: lambda: 'two', 42: lambda: 'the answer of life the universe and everything', }

then you add a default function: def default_case(): raise Exception('No case found!')

and you use the dictionary's get method to get the function given the value to check and run it. If value does not exists in dictionary, then default_case is run. >>> switch.get(1, default_case)() one >>> switch.get(2, default_case)() two >>> switch.get(3, default_case)() … Exception: No case found!

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>>> switch.get(42, default_case)() the answer of life the universe and everything

you can also make some syntactic sugar so the switch looks nicer: def run_switch(value): return switch.get(value, default_case)() >>> run_switch(1) one

Section 140.3: Use class introspection You can use a class to mimic the switch/case structure. The following is using introspection of a class (using the getattr() function that resolves a string into a bound method on an instance) to resolve the "case" part.

Then that introspecting method is aliased to the __call__ method to overload the () operator. class SwitchBase: def switch(self, case): m = getattr(self, 'case_{}'.format(case), None) if not m: return self.default return m __call__ = switch

Then to make it look nicer, we subclass the SwitchBase class (but it could be done in one class), and there we define all the case as methods: class CustomSwitcher: def case_1(self): return 'one' def case_2(self): return 'two' def case_42(self): return 'the answer of life, the universe and everything!' def default(self): raise Exception('Not a case!')

so then we can finally use it: >>> switch = CustomSwitcher() >>> print(switch(1)) one >>> print(switch(2)) two >>> print(switch(3)) … Exception: Not a case! >>> print(switch(42)) the answer of life, the universe and everything!

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Section 140.4: Using a context manager Another way, which is very readable and elegant, but far less efficient than an if/else structure, is to build a class such as follows, that will read and store the value to compare with, expose itself within the context as a callable that will return true if it matches the stored value: class Switch: def __init__(self, value): self._val = value def __enter__(self): return self def __exit__(self, type, value, traceback): return False # Allows traceback to occur def __call__(self, cond, *mconds): return self._val in (cond,)+mconds

then defining the cases is almost a match to the real switch/case construct (exposed within a function below, to make it easier to show off): def run_switch(value): with Switch(value) as case: if case(1): return 'one' if case(2): return 'two' if case(3): return 'the answer to the question about life, the universe and everything' # default raise Exception('Not a case!')

So the execution would be: >>> run_switch(1) one >>> run_switch(2) two >>> run_switch(3) … Exception: Not a case! >>> run_switch(42) the answer to the question about life, the universe and everything

Nota Bene: This solution is being offered as the switch module available on pypi.

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Chapter 141: List destructuring (aka packing and unpacking) Section 141.1: Destructuring assignment In assignments, you can split an Iterable into values using the "unpacking" syntax: Destructuring as values a, b = (1, 2) print(a) # Prints: 1 print(b) # Prints: 2

If you try to unpack more than the length of the iterable, you'll get an error: a, b, c = [1] # Raises: ValueError: not enough values to unpack (expected 3, got 1)

Python 3.x Version

> 3.0

Destructuring as a list You can unpack a list of unknown length using the following syntax: head, *tail = [1, 2, 3, 4, 5]

Here, we extract the first value as a scalar, and the other values as a list: print(head) # Prints: 1 print(tail) # Prints: [2, 3, 4, 5]

Which is equivalent to: l = [1, 2, 3, 4, 5] head = l[0] tail = l[1:]

It also works with multiple elements or elements form the end of the list: a, b, *other, z = [1, 2, 3, 4, 5] print(a, b, z, other) # Prints: 1 2 5 [3, 4]

Ignoring values in destructuring assignments If you're only interested in a given value, you can use _ to indicate you aren’t interested. Note: this will still set _, just most people don’t use it as a variable. a, _ = [1, 2] print(a) # Prints: 1 a, _, c = (1, 2, 3) print(a) # Prints: 1

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print(c) # Prints: 3

Python 3.x Version

> 3.0

Ignoring lists in destructuring assignments Finally, you can ignore many values using the *_ syntax in the assignment: a, *_ = [1, 2, 3, 4, 5] print(a) # Prints: 1

which is not really interesting, as you could using indexing on the list instead. Where it gets nice is to keep first and last values in one assignment: a, *_, b = [1, 2, 3, 4, 5] print(a, b) # Prints: 1 5

or extract several values at once: a, _, b, _, c, *_ = [1, 2, 3, 4, 5, 6] print(a, b, c) # Prints: 1 3 5

Section 141.2: Packing function arguments In functions, you can define a number of mandatory arguments: def fun1(arg1, arg2, arg3): return (arg1,arg2,arg3)

which will make the function callable only when the three arguments are given: fun1(1, 2, 3)

and you can define the arguments as optional, by using default values: def fun2(arg1='a', arg2='b', arg3='c'): return (arg1,arg2,arg3)

so you can call the function in many different ways, like: fun2(1)

→ (1,b,c)

fun2(1, 2)

→ (1,2,c)

fun2(arg2=2, arg3=3) → (a,2,3) ...

But you can also use the destructuring syntax to pack arguments up, so you can assign variables using a list or a dict.

Packing a list of arguments Consider you have a list of values

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l = [1,2,3]

You can call the function with the list of values as an argument using the * syntax: fun1(*l) # Returns: (1,2,3) fun1(*['w', 't', 'f']) # Returns: ('w','t','f')

But if you do not provide a list which length matches the number of arguments: fun1(*['oops']) # Raises: TypeError: fun1() missing 2 required positional arguments: 'arg2' and 'arg3'

Packing keyword arguments Now, you can also pack arguments using a dictionary. You can use the ** operator to tell Python to unpack the dict as parameter values: d = { 'arg1': 1, 'arg2': 2, 'arg3': 3 } fun1(**d) # Returns: (1, 2, 3)

when the function only has positional arguments (the ones without default values) you need the dictionary to be contain of all the expected parameters, and have no extra parameter, or you'll get an error: fun1(**{'arg1':1, 'arg2':2}) # Raises: TypeError: fun1() missing 1 required positional argument: 'arg3' fun1(**{'arg1':1, 'arg2':2, 'arg3':3, 'arg4':4}) # Raises: TypeError: fun1() got an unexpected keyword argument 'arg4'

For functions that have optional arguments, you can pack the arguments as a dictionary the same way: fun2(**d) # Returns: (1, 2, 3)

But there you can omit values, as they will be replaced with the defaults: fun2(**{'arg2': 2}) # Returns: ('a', 2, 'c')

And the same as before, you cannot give extra values that are not existing parameters: fun2(**{'arg1':1, 'arg2':2, 'arg3':3, 'arg4':4}) # Raises: TypeError: fun2() got an unexpected keyword argument 'arg4'

In real world usage, functions can have both positional and optional arguments, and it works the same: def fun3(arg1, arg2='b', arg3='c') return (arg1, arg2, arg3)

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you can call the function with just an iterable: fun3(*[1]) # Returns: (1, 'b', 'c') fun3(*[1,2,3]) # Returns: (1, 2, 3)

or with just a dictionary: fun3(**{'arg1':1}) # Returns: (1, 'b', 'c') fun3(**{'arg1':1, 'arg2':2, 'arg3':3}) # Returns: (1, 2, 3)

or you can use both in the same call: fun3(*[1,2], **{'arg3':3}) # Returns: (1,2,3)

Beware though that you cannot provide multiple values for the same argument: fun3(*[1,2], **{'arg2':42, 'arg3':3}) # Raises: TypeError: fun3() got multiple values for argument 'arg2'

Section 141.3: Unpacking function arguments When you want to create a function that can accept any number of arguments, and not enforce the position or the name of the argument at "compile" time, it's possible and here's how: def fun1(*args, **kwargs): print(args, kwargs)

The *args and **kwargs parameters are special parameters that are set to a tuple and a dict, respectively: fun1(1,2,3) # Prints: (1, 2, 3) {} fun1(a=1, b=2, c=3) # Prints: () {'a': 1, 'b': 2, 'c': 3} fun1('x', 'y', 'z', a=1, b=2, c=3) # Prints: ('x', 'y', 'z') {'a': 1, 'b': 2, 'c': 3}

If you look at enough Python code, you'll quickly discover that it is widely being used when passing arguments over to another function. For example if you want to extend the string class: class MyString(str): def __init__(self, *args, **kwarg): print('Constructing MyString') super(MyString, self).__init__(*args, **kwarg)

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Chapter 142: Accessing Python source code and bytecode Section 142.1: Display the bytecode of a function The Python interpreter compiles code to bytecode before executing it on the Python's virtual machine (see also What is python bytecode?. Here's how to view the bytecode of a Python function import dis def fib(n): if n >> book1.title 'P.G. Wodehouse'

If an attribute doesn't exist, Python throws an error: >>> book1.series Traceback (most recent call last): File "", line 1, in AttributeError: 'Book' object has no attribute 'series'

Section 144.2: Setters, Getters & Properties For the sake of data encapsulation, sometimes you want to have an attribute which value comes from other attributes or, in general, which value shall be computed at the moment. The standard way to deal with this situation is to create a method, called getter or a setter. class Book: def __init__(self, title, author): self.title = title self.author = author

In the example above, it's easy to see what happens if we create a new Book that contains a title and a author. If all books we're to add to our Library have authors and titles, then we can skip the getters and setters and use the dot notation. However, suppose we have some books that do not have an author and we want to set the author to "Unknown". Or if they have multiple authors and we plan to return a list of authors. In this case we can create a getter and a setter for the author attribute. class P: def __init__(self,title,author): self.title = title self.setAuthor(author) def get_author(self): return self.author def set_author(self, author): if not author: self.author = "Unknown" else:

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self.author = author

This scheme is not recommended. One reason is that there is a catch: Let's assume we have designed our class with the public attribute and no methods. People have already used it a lot and they have written code like this: >>> book = Book(title="Ancient Manuscript", author="Some Guy") >>> book.author = "" #Cos Some Guy didn't write this one!

Now we have a problem. Because author is not an attribute! Python offers a solution to this problem called properties. A method to get properties is decorated with the @property before it's header. The method that we want to function as a setter is decorated with @attributeName.setter before it. Keeping this in mind, we now have our new updated class. class Book: def __init__(self, title, author): self.title = title self.author = author @property def author(self): return self.__author @author.setter def author(self, author): if not author: self.author = "Unknown" else: self.author = author

Note, normally Python doesn't allow you to have multiple methods with the same name and different number of parameters. However, in this case Python allows this because of the decorators used. If we test the code: >>> book = Book(title="Ancient Manuscript", author="Some Guy") >>> book.author = "" #Cos Some Guy didn't write this one! >>> book.author Unknown

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Chapter 145: ArcPy Section 145.1: createDissolvedGDB to create a file gdb on the workspace def createDissolvedGDB(workspace, gdbName): gdb_name = workspace + "/" + gdbName + ".gdb" if(arcpy.Exists(gdb_name): arcpy.Delete_management(gdb_name) arcpy.CreateFileGDB_management(workspace, gdbName, "") else: arcpy.CreateFileGDB_management(workspace, gdbName, "") return gdb_name

Section 145.2: Printing one field's value for all rows of feature class in file geodatabase using Search Cursor To print a test field (TestField) from a test feature class (TestFC) in a test file geodatabase (Test.gdb) located in a temporary folder (C:\Temp): with arcpy.da.SearchCursor(r"C:\Temp\Test.gdb\TestFC",["TestField"]) as cursor: for row in cursor: print row[0]

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Chapter 146: Abstract Base Classes (abc) Section 146.1: Setting the ABCMeta metaclass Abstract classes are classes that are meant to be inherited but avoid implementing specific methods, leaving behind only method signatures that subclasses must implement. Abstract classes are useful for defining and enforcing class abstractions at a high level, similar to the concept of interfaces in typed languages, without the need for method implementation. One conceptual approach to defining an abstract class is to stub out the class methods, and then raise a NotImplementedError if accessed. This prevents children classes from accessing parent methods without overriding them first. Like so: class Fruit: def check_ripeness(self): raise NotImplementedError("check_ripeness method not implemented!")

class Apple(Fruit): pass

a = Apple() a.check_ripeness() # raises NotImplementedError

Creating an abstract class in this way prevents improper usage of methods that are not overridden, and certainly encourages methods to be defined in child classes, but it does not enforce their definition. With the abc module we can prevent child classes from being instantiated when they fail to override abstract class methods of their parents and ancestors: from abc import ABCMeta class AbstractClass(object): # the metaclass attribute must always be set as a class variable __metaclass__ = ABCMeta # the abstractmethod decorator registers this method as undefined @abstractmethod def virtual_method_subclasses_must_define(self): # Can be left completely blank, or a base implementation can be provided # Note that ordinarily a blank interpretation implicitly returns `None`, # but by registering, this behaviour is no longer enforced.

It is now possible to simply subclass and override: class Subclass(AbstractClass): def virtual_method_subclasses_must_define(self): return

Section 146.2: Why/How to use ABCMeta and @abstractmethod Abstract base classes (ABCs) enforce what derived classes implement particular methods from the base class. GoalKicker.com – Python® Notes for Professionals

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To understand how this works and why we should use it, let's take a look at an example that Van Rossum would enjoy. Let's say we have a Base class "MontyPython" with two methods (joke & punchline) that must be implemented by all derived classes. class MontyPython: def joke(self): raise NotImplementedError() def punchline(self): raise NotImplementedError() class ArgumentClinic(MontyPython): def joke(self): return "Hahahahahah"

When we instantiate an object and call it's two methods, we'll get an error (as expected) with the punchline() method. >>> sketch = ArgumentClinic() >>> sketch.punchline() NotImplementedError

However, this still allows us to instantiate an object of the ArgumentClinic class without getting an error. In fact we don't get an error until we look for the punchline(). This is avoided by using the Abstract Base Class (ABC) module. Let's see how this works with the same example: from abc import ABCMeta, abstractmethod class MontyPython(metaclass=ABCMeta): @abstractmethod def joke(self): pass @abstractmethod def punchline(self): pass class ArgumentClinic(MontyPython): def joke(self): return "Hahahahahah"

This time when we try to instantiate an object from the incomplete class, we immediately get a TypeError! >>> c = ArgumentClinic() TypeError: "Can't instantiate abstract class ArgumentClinic with abstract methods punchline"

In this case, it's easy to complete the class to avoid any TypeErrors: class ArgumentClinic(MontyPython): def joke(self): return "Hahahahahah" def punchline(self): return "Send in the constable!"

This time when you instantiate an object it works! GoalKicker.com – Python® Notes for Professionals

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Chapter 147: Plugin and Extension Classes Section 147.1: Mixins In Object oriented programming language, a mixin is a class that contains methods for use by other classes without having to be the parent class of those other classes. How those other classes gain access to the mixin's methods depends on the language. It provides a mechanism for multiple inheritance by allowing multiple classes to use the common functionality, but without the complex semantics of multiple inheritance. Mixins are useful when a programmer wants to share functionality between different classes. Instead of repeating the same code over and over again, the common functionality can simply be grouped into a mixin and then inherited into each class that requires it. When we use more than one mixins, Order of mixins are important. here is a simple example: class Mixin1(object): def test(self): print "Mixin1" class Mixin2(object): def test(self): print "Mixin2" class MyClass(Mixin1, Mixin2): pass

In this example we call MyClass and test method, >>> obj = MyClass() >>> obj.test() Mixin1

Result must be Mixin1 because Order is left to right. This could be show unexpected results when super classes add with it. So reverse order is more good just like this: class MyClass(Mixin2, Mixin1): pass

Result will be: >>> obj = MyClass() >>> obj.test() Mixin2

Mixins can be used to define custom plugins. Python 3.x Version

≥ 3.0

class Base(object): def test(self): print("Base.") class PluginA(object): def test(self): super().test() print("Plugin A.")

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class PluginB(object): def test(self): super().test() print("Plugin B.") plugins = PluginA, PluginB class PluginSystemA(PluginA, Base): pass class PluginSystemB(PluginB, Base): pass PluginSystemA().test() # Base. # Plugin A. PluginSystemB().test() # Base. # Plugin B.

Section 147.2: Plugins with Customized Classes In Python 3.6, PEP 487 added the __init_subclass__ special method, which simplifies and extends class customization without using metaclasses. Consequently, this feature allows for creating simple plugins. Here we demonstrate this feature by modifying a prior example: Python 3.x Version

≥ 3.6

class Base: plugins = [] def __init_subclass__(cls, **kwargs): super().__init_subclass__(**kwargs) cls.plugins.append(cls) def test(self): print("Base.") class PluginA(Base): def test(self): super().test() print("Plugin A.")

class PluginB(Base): def test(self): super().test() print("Plugin B.")

Results: PluginA().test() # Base. # Plugin A. PluginB().test() # Base. # Plugin B. Base.plugins

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# [__main__.PluginA, __main__.PluginB]

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Chapter 148: Immutable datatypes(int, float, str, tuple and frozensets) Section 148.1: Individual characters of strings are not assignable foo = "bar" foo[0] = "c" # Error

Immutable variable value can not be changed once they are created.

Section 148.2: Tuple's individual members aren't assignable foo = ("bar", 1, "Hello!",) foo[1] = 2 # ERROR!!

Second line would return an error since tuple members once created aren't assignable. Because of tuple's immutability.

Section 148.3: Frozenset's are immutable and not assignable foo = frozenset(["bar", 1, "Hello!"]) foo[2] = 7 # ERROR foo.add(3) # ERROR

Second line would return an error since frozenset members once created aren't assignable. Third line would return error as frozensets do not support functions that can manipulate members.

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Chapter 149: Incompatibilities moving from Python 2 to Python 3 Unlike most languages, Python supports two major versions. Since 2008 when Python 3 was released, many have made the transition, while many have not. In order to understand both, this section covers the important differences between Python 2 and Python 3.

Section 149.1: Integer Division The standard division symbol (/) operates differently in Python 3 and Python 2 when applied to integers. When dividing an integer by another integer in Python 3, the division operation x / y represents a true division (uses __truediv__ method) and produces a floating point result. Meanwhile, the same operation in Python 2 represents a classic division that rounds the result down toward negative infinity (also known as taking the floor). For example:

3 / 2 1

Code Python 2 output

Python 3 output 1.5

2 / 3 0

0.6666666666666666

-3 / 2 -2

-1.5

The rounding-towards-zero behavior was deprecated in Python 2.2, but remains in Python 2.7 for the sake of backward compatibility and was removed in Python 3. Note: To get a float result in Python 2 (without floor rounding) we can specify one of the operands with the decimal point. The above example of 2/3 which gives 0 in Python 2 shall be used as 2 / 3.0 or 2.0 / 3 or 2.0/3.0 to get 0.6666666666666666

Code

Python 2 output

3.0 / 2.0 1.5 2 / 3.0

Python 3 output 1.5

0.6666666666666666 0.6666666666666666

-3.0 / 2 -1.5

-1.5

There is also the floor division operator (//), which works the same way in both versions: it rounds down to the nearest integer. (although a float is returned when used with floats) In both versions the // operator maps to __floordiv__.

3 // 2

Code

Python 2 output Python 3 output 1 1

2 // 3

0

0

-3 // 2

-2

-2

3.0 // 2.0 1.0

1.0

0.0

0.0

2.0 // 3

-3 // 2.0 -2.0

-2.0

One can explicitly enforce true division or floor division using native functions in the operator module: from operator import truediv, floordiv assert truediv(10, 8) == 1.25 assert floordiv(10, 8) == 1

# equivalent to `/` in Python 3 # equivalent to `//`

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While clear and explicit, using operator functions for every division can be tedious. Changing the behavior of the / operator will often be preferred. A common practice is to eliminate typical division behavior by adding from __future__ import division as the first statement in each module: # needs to be the first statement in a module from __future__ import division

Code

Python 2 output 3 / 2 1.5

Python 3 output 1.5

2 / 3 0.6666666666666666 0.6666666666666666 -3 / 2 -1.5

-1.5

from __future__ import division guarantees that the / operator represents true division and only within the

modules that contain the __future__ import, so there are no compelling reasons for not enabling it in all new modules. Note: Some other programming languages use rounding toward zero (truncation) rather than rounding down toward negative infinity as Python does (i.e. in those languages -3 / 2 == -1). This behavior may create confusion when porting or comparing code. Note on float operands: As an alternative to from __future__ import division, one could use the usual division symbol / and ensure that at least one of the operands is a float: 3 / 2.0 == 1.5. However, this can be considered bad practice. It is just too easy to write average = sum(items) / len(items) and forget to cast one of the arguments to float. Moreover, such cases may frequently evade notice during testing, e.g., if you test on an array containing floats but receive an array of ints in production. Additionally, if the same code is used in Python 3, programs that expect 3 / 2 == 1 to be True will not work correctly. See PEP 238 for more detailed rationale why the division operator was changed in Python 3 and why old-style division should be avoided. See the Simple Math topic for more about division.

Section 149.2: Unpacking Iterables Python 3.x Version

≥ 3.0

In Python 3, you can unpack an iterable without knowing the exact number of items in it, and even have a variable hold the end of the iterable. For that, you provide a variable that may collect a list of values. This is done by placing an asterisk before the name. For example, unpacking a list: first, second, *tail, last = [1, 2, 3, 4, 5] print(first) # Out: 1 print(second) # Out: 2 print(tail) # Out: [3, 4] print(last) # Out: 5

Note: When using the *variable syntax, the variable will always be a list, even if the original type wasn't a list. It may contain zero or more elements depending on the number of elements in the original list. first, second, *tail, last = [1, 2, 3, 4] print(tail)

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# Out: [3] first, second, *tail, last = [1, 2, 3] print(tail) # Out: [] print(last) # Out: 3

Similarly, unpacking a str: begin, *tail = "Hello" print(begin) # Out: 'H' print(tail) # Out: ['e', 'l', 'l', 'o']

Example of unpacking a date; _ is used in this example as a throwaway variable (we are interested only in year value): person = ('John', 'Doe', (10, 16, 2016)) *_, (*_, year_of_birth) = person print(year_of_birth) # Out: 2016

It is worth mentioning that, since * eats up a variable number of items, you cannot have two *s for the same iterable in an assignment - it wouldn't know how many elements go into the first unpacking, and how many in the second: *head, *tail = [1, 2] # Out: SyntaxError: two starred expressions in assignment

Python 3.x Version

≥ 3.5

So far we have discussed unpacking in assignments. * and ** were extended in Python 3.5. It's now possible to have several unpacking operations in one expression: {*range(4), 4, *(5, 6, 7)} # Out: {0, 1, 2, 3, 4, 5, 6, 7}

Python 2.x Version

≥ 2.0

It is also possible to unpack an iterable into function arguments: iterable = [1, 2, 3, 4, 5] print(iterable) # Out: [1, 2, 3, 4, 5] print(*iterable) # Out: 1 2 3 4 5

Python 3.x Version

≥ 3.5

Unpacking a dictionary uses two adjacent stars ** (PEP 448): tail = {'y': 2, 'z': 3} {'x': 1, **tail} # Out: {'x': 1, 'y': 2, 'z': 3}

This allows for both overriding old values and merging dictionaries. dict1 = {'x': 1, 'y': 1}

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dict2 = {'y': 2, 'z': 3} {**dict1, **dict2} # Out: {'x': 1, 'y': 2, 'z': 3}

Python 3.x Version

≥ 3.0

Python 3 removed tuple unpacking in functions. Hence the following doesn't work in Python 3 # Works in Python 2, but syntax error in Python 3: map(lambda (x, y): x + y, zip(range(5), range(5))) # Same is true for non-lambdas: def example((x, y)): pass # Works in both Python 2 and Python 3: map(lambda x: x[0] + x[1], zip(range(5), range(5))) # And non-lambdas, too: def working_example(x_y): x, y = x_y pass

See PEP 3113 for detailed rationale.

Section 149.3: Strings: Bytes versus Unicode Python 2.x Version

≤ 2.7

In Python 2 there are two variants of string: those made of bytes with type (str) and those made of text with type (unicode). In Python 2, an object of type str is always a byte sequence, but is commonly used for both text and binary data. A string literal is interpreted as a byte string. s = 'Cafe'

# type(s) == str

There are two exceptions: You can define a Unicode (text) literal explicitly by prefixing the literal with u: s = u'Café' # type(s) == unicode b = 'Lorem ipsum' # type(b) == str

Alternatively, you can specify that a whole module's string literals should create Unicode (text) literals: from __future__ import unicode_literals s = 'Café' # type(s) == unicode b = 'Lorem ipsum' # type(b) == unicode

In order to check whether your variable is a string (either Unicode or a byte string), you can use: isinstance(s, basestring)

Python 3.x Version

≥ 3.0

In Python 3, the str type is a Unicode text type. s = 'Cafe'

# type(s) == str

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s = 'Café'

# type(s) == str (note the accented trailing e)

Additionally, Python 3 added a bytes object, suitable for binary "blobs" or writing to encoding-independent files. To create a bytes object, you can prefix b to a string literal or call the string's encode method: # Or, if you really need a byte string: s = b'Cafe' # type(s) == bytes s = 'Café'.encode() # type(s) == bytes

To test whether a value is a string, use: isinstance(s, str)

Python 3.x Version

≥ 3.3

It is also possible to prefix string literals with a u prefix to ease compatibility between Python 2 and Python 3 code bases. Since, in Python 3, all strings are Unicode by default, prepending a string literal with u has no effect: u'Cafe' == 'Cafe'

Python 2’s raw Unicode string prefix ur is not supported, however: >>> ur'Café' File "", line 1 ur'Café' ^ SyntaxError: invalid syntax

Note that you must encode a Python 3 text (str) object to convert it into a bytes representation of that text. The default encoding of this method is UTF-8. You can use decode to ask a bytes object for what Unicode text it represents: >>> b.decode() 'Café'

Python 2.x Version

≥ 2.6

While the bytes type exists in both Python 2 and 3, the unicode type only exists in Python 2. To use Python 3's implicit Unicode strings in Python 2, add the following to the top of your code file: from __future__ import unicode_literals print(repr("hi")) # u'hi'

Python 3.x Version

≥ 3.0

Another important difference is that indexing bytes in Python 3 results in an int output like so: b"abc"[0] == 97

Whilst slicing in a size of one results in a length 1 bytes object: b"abc"[0:1] == b"a"

In addition, Python 3 fixes some unusual behavior with unicode, i.e. reversing byte strings in Python 2. For example, the following issue is resolved: GoalKicker.com – Python® Notes for Professionals

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# -*- coding: utf8 -*print("Hi, my name is Łukasz Langa.") print(u"Hi, my name is Łukasz Langa."[::-1]) print("Hi, my name is Łukasz Langa."[::-1]) # Output in Python 2 # Hi, my name is Łukasz Langa. # .agnaL zsakuŁ si eman ym ,iH # .agnaL zsaku�� si eman ym ,iH # Output in Python 3 # Hi, my name is Łukasz Langa. # .agnaL zsakuŁ si eman ym ,iH # .agnaL zsakuŁ si eman ym ,iH

Section 149.4: Print statement vs. Print function In Python 2, print is a statement: Python 2.x Version

≤ 2.7

print "Hello World" print print "No newline", print >>sys.stderr, "Error" print("hello") print() print 1, 2, 3 print(1, 2, 3)

# # # # # # #

print a newline add trailing comma to remove newline print to stderr print "hello", since ("hello") == "hello" print an empty tuple "()" print space-separated arguments: "1 2 3" print tuple "(1, 2, 3)"

In Python 3, print() is a function, with keyword arguments for common uses: Python 3.x Version

≥ 3.0

print "Hello World" # SyntaxError print("Hello World") print() # print a newline (must use parentheses) print("No newline", end="") # end specifies what to append (defaults to newline) print("Error", file=sys.stderr) # file specifies the output buffer print("Comma", "separated", "output", sep=",") # sep specifies the separator print("A", "B", "C", sep="") # null string for sep: prints as ABC print("Flush this", flush=True) # flush the output buffer, added in Python 3.3 print(1, 2, 3) # print space-separated arguments: "1 2 3" print((1, 2, 3)) # print tuple "(1, 2, 3)"

The print function has the following parameters: print(*objects, sep=' ', end='\n', file=sys.stdout, flush=False) sep is what separates the objects you pass to print. For example: print('foo', 'bar', sep='~') # out: foo~bar print('foo', 'bar', sep='.') # out: foo.bar end is what the end of the print statement is followed by. For example: print('foo', 'bar', end='!') # out: foo bar!

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Printing again following a non-newline ending print statement will print to the same line: print('foo', end='~') print('bar') # out: foo~bar

Note: For future compatibility, print function is also available in Python 2.6 onwards; however it cannot be used unless parsing of the print statement is disabled with from __future__ import print_function

This function has exactly same format as Python 3's, except that it lacks the flush parameter. See PEP 3105 for rationale.

Section 149.5: Dierences between range and xrange functions In Python 2, range function returns a list while xrange creates a special xrange object, which is an immutable sequence, which unlike other built-in sequence types, doesn't support slicing and has neither index nor count methods: Python 2.x Version

≥ 2.3

print(range(1, 10)) # Out: [1, 2, 3, 4, 5, 6, 7, 8, 9] print(isinstance(range(1, 10), list)) # Out: True print(xrange(1, 10)) # Out: xrange(1, 10) print(isinstance(xrange(1, 10), xrange)) # Out: True

In Python 3, xrange was expanded to the range sequence, which thus now creates a range object. There is no xrange type:

Python 3.x Version

≥ 3.0

print(range(1, 10)) # Out: range(1, 10) print(isinstance(range(1, 10), range)) # Out: True # print(xrange(1, 10)) # The output will be: #Traceback (most recent call last): # File "", line 1, in #NameError: name 'xrange' is not defined

Additionally, since Python 3.2, range also supports slicing, index and count: print(range(1, 10)[3:7]) # Out: range(3, 7) print(range(1, 10).count(5)) # Out: 1

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print(range(1, 10).index(7)) # Out: 6

The advantage of using a special sequence type instead of a list is that the interpreter does not have to allocate memory for a list and populate it: Python 2.x Version # # # # #

≥ 2.3

range(10000000000000000) The output would be: Traceback (most recent call last): File "", line 1, in MemoryError

print(xrange(100000000000000000)) # Out: xrange(100000000000000000)

Since the latter behaviour is generally desired, the former was removed in Python 3. If you still want to have a list in Python 3, you can simply use the list() constructor on a range object: Python 3.x Version

≥ 3.0

print(list(range(1, 10))) # Out: [1, 2, 3, 4, 5, 6, 7, 8, 9]

Compatibility In order to maintain compatibility between both Python 2.x and Python 3.x versions, you can use the builtins module from the external package future to achieve both forward-compatibility and backward-compatibility: Python 2.x Version

≥ 2.0

#forward-compatible from builtins import range for i in range(10**8): pass

Python 3.x Version

≥ 3.0

#backward-compatible from past.builtins import xrange for i in xrange(10**8): pass

The range in future library supports slicing, index and count in all Python versions, just like the built-in method on Python 3.2+.

Section 149.6: Raising and handling Exceptions This is the Python 2 syntax, note the commas , on the raise and except lines: Python 2.x Version

≥ 2.3

try: raise IOError, "input/output error" except IOError, exc: print exc

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try: raise IOError("input/output error") except IOError as exc: print(exc)

For backwards compatibility, the Python 3 syntax is also available in Python 2.6 onwards, so it should be used for all new code that does not need to be compatible with previous versions.

Python 3.x Version

≥ 3.0

Python 3 also adds exception chaining, wherein you can signal that some other exception was the cause for this exception. For example try: file = open('database.db') except FileNotFoundError as e: raise DatabaseError('Cannot open {}') from e

The exception raised in the except statement is of type DatabaseError, but the original exception is marked as the __cause__ attribute of that exception. When the traceback is displayed, the original exception will also be displayed

in the traceback: Traceback (most recent call last): File "", line 2, in FileNotFoundError The above exception was the direct cause of the following exception: Traceback (most recent call last): File "", line 4, in DatabaseError('Cannot open database.db')

If you throw in an except block without explicit chaining: try: file = open('database.db') except FileNotFoundError as e: raise DatabaseError('Cannot open {}')

The traceback is Traceback (most recent call last): File "", line 2, in FileNotFoundError During handling of the above exception, another exception occurred: Traceback (most recent call last): File "", line 4, in DatabaseError('Cannot open database.db')

Python 2.x Version

≥ 2.0

Neither one is supported in Python 2.x; the original exception and its traceback will be lost if another exception is raised in the except block. The following code can be used for compatibility: GoalKicker.com – Python® Notes for Professionals

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import sys import traceback try: funcWithError() except: sys_vers = getattr(sys, 'version_info', (0,)) if sys_vers < (3, 0): traceback.print_exc() raise Exception("new exception")

Python 3.x Version

≥ 3.3

To "forget" the previously thrown exception, use raise from None try: file = open('database.db') except FileNotFoundError as e: raise DatabaseError('Cannot open {}') from None

Now the traceback would simply be Traceback (most recent call last): File "", line 4, in DatabaseError('Cannot open database.db')

Or in order to make it compatible with both Python 2 and 3 you may use the six package like so: import six try: file = open('database.db') except FileNotFoundError as e: six.raise_from(DatabaseError('Cannot open {}'), None)

Section 149.7: Leaked variables in list comprehension Python 2.x Version

≥ 2.3

x = 'hello world!' vowels = [x for x in 'AEIOU'] print (vowels) # Out: ['A', 'E', 'I', 'O', 'U'] print(x) # Out: 'U'

Python 3.x Version

≥ 3.0

x = 'hello world!' vowels = [x for x in 'AEIOU'] print (vowels) # Out: ['A', 'E', 'I', 'O', 'U'] print(x) # Out: 'hello world!'

As can be seen from the example, in Python 2 the value of x was leaked: it masked hello world! and printed out U, since this was the last value of x when the loop ended. However, in Python 3 x prints the originally defined hello world!, since the local variable from the list GoalKicker.com – Python® Notes for Professionals

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comprehension does not mask variables from the surrounding scope. Additionally, neither generator expressions (available in Python since 2.5) nor dictionary or set comprehensions (which were backported to Python 2.7 from Python 3) leak variables in Python 2. Note that in both Python 2 and Python 3, variables will leak into the surrounding scope when using a for loop: x = 'hello world!' vowels = [] for x in 'AEIOU': vowels.append(x) print(x) # Out: 'U'

Section 149.8: True, False and None In Python 2, True, False and None are built-in constants. Which means it's possible to reassign them. Python 2.x Version

≥ 2.0

True, False = False, True True # False False # True

You can't do this with None since Python 2.4. Python 2.x Version None = None

≥ 2.4

# SyntaxError: cannot assign to None

In Python 3, True, False, and None are now keywords. Python 3.x Version

≥ 3.0

True, False = False, True None = None

# SyntaxError: can't assign to keyword

# SyntaxError: can't assign to keyword

Section 149.9: User Input In Python 2, user input is accepted using the raw_input function, Python 2.x Version

≥ 2.3

user_input = raw_input()

While in Python 3 user input is accepted using the input function. Python 3.x Version

≥ 3.0

user_input = input()

In Python 2, the input function will accept input and interpret it. While this can be useful, it has several security considerations and was removed in Python 3. To access the same functionality, eval(input()) can be used. To keep a script portable across the two versions, you can put the code below near the top of your Python script: try: input = raw_input except NameError:

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pass

Section 149.10: Comparison of dierent types Python 2.x Version

≥ 2.3

Objects of different types can be compared. The results are arbitrary, but consistent. They are ordered such that None is less than anything else, numeric types are smaller than non-numeric types, and everything else is ordered

lexicographically by type. Thus, an int is less than a str and a tuple is greater than a list: [1, 2] > 'foo' # Out: False (1, 2) > 'foo' # Out: True [1, 2] > (1, 2) # Out: False 100 < [1, 'x'] < 'xyz' < (1, 'x') # Out: True

This was originally done so a list of mixed types could be sorted and objects would be grouped together by type: l = [7, 'x', (1, 2), [5, 6], 5, 8.0, 'y', 1.2, [7, 8], 'z'] sorted(l) # Out: [1.2, 5, 7, 8.0, [5, 6], [7, 8], 'x', 'y', 'z', (1, 2)]

Python 3.x Version

≥ 3.0

An exception is raised when comparing different (non-numeric) types: 1 < 1.5 # Out: True [1, 2] > 'foo' # TypeError: unorderable types: list() > str() (1, 2) > 'foo' # TypeError: unorderable types: tuple() > str() [1, 2] > (1, 2) # TypeError: unorderable types: list() > tuple()

To sort mixed lists in Python 3 by types and to achieve compatibility between versions, you have to provide a key to the sorted function: >>> list = [1, 'hello', [3, 4], {'python': 2}, 'stackoverflow', 8, {'python': 3}, [5, 6]] >>> sorted(list, key=str) # Out: [1, 8, [3, 4], [5, 6], 'hello', 'stackoverflow', {'python': 2}, {'python': 3}]

Using str as the key function temporarily converts each item to a string only for the purposes of comparison. It then sees the string representation starting with either [, ', { or 0-9 and it's able to sort those (and all the following characters).

Section 149.11: .next() method on iterators renamed In Python 2, an iterator can be traversed by using a method called next on the iterator itself: Python 2.x Version

≥ 2.3

g = (i for i in range(0, 3)) g.next() # Yields 0

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g.next() g.next()

# Yields 1 # Yields 2

In Python 3 the .next method has been renamed to .__next__, acknowledging its “magic” role, so calling .next will raise an AttributeError. The correct way to access this functionality in both Python 2 and Python 3 is to call the next function with the iterator as an argument.

Python 3.x Version g = (i for next(g) # next(g) # next(g) #

≥ 3.0

i in range(0, 3)) Yields 0 Yields 1 Yields 2

This code is portable across versions from 2.6 through to current releases.

Section 149.12: filter(), map() and zip() return iterators instead of sequences Python 2.x Version

≤ 2.7

In Python 2 filter, map and zip built-in functions return a sequence. map and zip always return a list while with filter the return type depends on the type of given parameter: >>> s = filter(lambda x: x.isalpha(), 'a1b2c3') >>> s 'abc' >>> s = map(lambda x: x * x, [0, 1, 2]) >>> s [0, 1, 4] >>> s = zip([0, 1, 2], [3, 4, 5]) >>> s [(0, 3), (1, 4), (2, 5)]

Python 3.x Version

≥ 3.0

In Python 3 filter, map and zip return iterator instead: >>> it = filter(lambda x: x.isalpha(), 'a1b2c3') >>> it

>>> ''.join(it) 'abc' >>> it = map(lambda x: x * x, [0, 1, 2]) >>> it

>>> list(it) [0, 1, 4] >>> it = zip([0, 1, 2], [3, 4, 5]) >>> it

>>> list(it) [(0, 3), (1, 4), (2, 5)]

Since Python 2 itertools.izip is equivalent of Python 3 zip izip has been removed on Python 3.

Section 149.13: Renamed modules A few modules in the standard library have been renamed: GoalKicker.com – Python® Notes for Professionals

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Old name _winreg

New name winreg

ConfigParser

configparser

copy_reg

copyreg

Queue

queue

SocketServer

socketserver

_markupbase

markupbase

repr

reprlib

test.test_support test.support Tkinter

tkinter

tkFileDialog

tkinter.filedialog

urllib / urllib2

urllib, urllib.parse, urllib.error, urllib.response, urllib.request, urllib.robotparser

Some modules have even been converted from files to libraries. Take tkinter and urllib from above as an example. Compatibility When maintaining compatibility between both Python 2.x and 3.x versions, you can use the future external package to enable importing top-level standard library packages with Python 3.x names on Python 2.x versions.

Section 149.14: Removed operators and ``, synonymous with != and repr() In Python 2, is a synonym for !=; likewise, `foo` is a synonym for repr(foo). Python 2.x Version

≤ 2.7

>>> 1 2 True >>> 1 1 False >>> foo = 'hello world' >>> repr(foo) "'hello world'" >>> `foo` "'hello world'"

Python 3.x Version

≥ 3.0

>>> 1 2 File "", line 1 1 2 ^ SyntaxError: invalid syntax >>> `foo` File "", line 1 `foo` ^ SyntaxError: invalid syntax

Section 149.15: long vs. int In Python 2, any integer larger than a C ssize_t would be converted into the long data type, indicated by an L suffix on the literal. For example, on a 32 bit build of Python: Python 2.x Version

≤ 2.7

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>>> 2**31 2147483648L >>> type(2**31)

>>> 2**30 1073741824 >>> type(2**30)

>>> 2**31 - 1 # 2**31 is long and long - int is long 2147483647L

However, in Python 3, the long data type was removed; no matter how big the integer is, it will be an int. Python 3.x Version

≥ 3.0

2**1024 # Output: 17976931348623159077293051907890247336179769789423065727343008115773267580550096313270847732240753602 11201138798713933576587897688144166224928474306394741243777678934248654852763022196012460941194530829 52085005768838150682342462881473913110540827237163350510684586298239947245938479716304835356329624224 137216 print(-(2**1024)) # Output: -1797693134862315907729305190789024733617976978942306572734300811577326758055009631327084773224075360 21120113879871393357658789768814416622492847430639474124377767893424865485276302219601246094119453082 95208500576883815068234246288147391311054082723716335051068458629823994724593847971630483535632962422 4137216 type(2**1024) # Output:

Section 149.16: All classes are "new-style classes" in Python 3 In Python 3.x all classes are new-style classes; when defining a new class python implicitly makes it inherit from object. As such, specifying object in a class definition is a completely optional:

Python 3.x Version

≥ 3.0

class X: pass class Y(object): pass

Both of these classes now contain object in their mro (method resolution order): Python 3.x Version

≥ 3.0

>>> X.__mro__ (__main__.X, object) >>> Y.__mro__ (__main__.Y, object)

In Python 2.x classes are, by default, old-style classes; they do not implicitly inherit from object. This causes the semantics of classes to differ depending on if we explicitly add object as a base class: Python 2.x Version

≥ 2.3

class X: pass class Y(object): pass

In this case, if we try to print the __mro__ of Y, similar output as that in the Python 3.x case will appear: Python 2.x Version

≥ 2.3

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>>> Y.__mro__ (, )

This happens because we explicitly made Y inherit from object when defining it: class Y(object): pass. For class X which does not inherit from object the __mro__ attribute does not exist, trying to access it results in an AttributeError.

In order to ensure compatibility between both versions of Python, classes can be defined with object as a base class: class mycls(object): """I am fully compatible with Python 2/3"""

Alternatively, if the __metaclass__ variable is set to type at global scope, all subsequently defined classes in a given module are implicitly new-style without needing to explicitly inherit from object: __metaclass__ = type class mycls: """I am also fully compatible with Python 2/3"""

Section 149.17: Reduce is no longer a built-in In Python 2, reduce is available either as a built-in function or from the functools package (version 2.6 onwards), whereas in Python 3 reduce is available only from functools. However the syntax for reduce in both Python2 and Python3 is the same and is reduce(function_to_reduce, list_to_reduce). As an example, let us consider reducing a list to a single value by dividing each of the adjacent numbers. Here we use truediv function from the operator library. In Python 2.x it is as simple as: Python 2.x Version

≥ 2.3

>>> my_list = [1, 2, 3, 4, 5] >>> import operator >>> reduce(operator.truediv, my_list) 0.008333333333333333

In Python 3.x the example becomes a bit more complicated: Python 3.x Version

≥ 3.0

>>> my_list = [1, 2, 3, 4, 5] >>> import operator, functools >>> functools.reduce(operator.truediv, my_list) 0.008333333333333333

We can also use from functools import reduce to avoid calling reduce with the namespace name.

Section 149.18: Absolute/Relative Imports In Python 3, PEP 404 changes the way imports work from Python 2. Implicit relative imports are no longer allowed in packages and from ... import * imports are only allowed in module level code. To achieve Python 3 behavior in Python 2: GoalKicker.com – Python® Notes for Professionals

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the absolute imports feature can be enabled with from __future__ import absolute_import explicit relative imports are encouraged in place of implicit relative imports For clarification, in Python 2, a module can import the contents of another module located in the same directory as follows: import foo

Notice the location of foo is ambiguous from the import statement alone. This type of implicit relative import is thus discouraged in favor of explicit relative imports, which look like the following: from from from from from from from from

.moduleY import spam .moduleY import spam as ham . import moduleY ..subpackage1 import moduleY ..subpackage2.moduleZ import eggs ..moduleA import foo ...package import bar ...sys import path

The dot . allows an explicit declaration of the module location within the directory tree.

More on Relative Imports Consider some user defined package called shapes. The directory structure is as follows: shapes ├── __init__.py | ├── circle.py | ├── square.py | └── triangle.py circle.py, square.py and triangle.py all import util.py as a module. How will they refer to a module in the

same level? from . import util # use util.PI, util.sq(x), etc

OR from .util import * #use PI, sq(x), etc to call functions

The . is used for same-level relative imports. Now, consider an alternate layout of the shapes module: shapes ├── __init__.py | ├── circle │

├── __init__.py

│ |

└── circle.py

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├── square │

├── __init__.py

│ └── square.py | ├── triangle │

├── __init__.py

│ ├── triangle.py | └── util.py

Now, how will these 3 classes refer to util.py? from .. import util # use util.PI, util.sq(x), etc

OR from ..util import * # use PI, sq(x), etc to call functions

The .. is used for parent-level relative imports. Add more .s with number of levels between the parent and child.

Section 149.19: map() map() is a builtin that is useful for applying a function to elements of an iterable. In Python 2, map returns a list. In

Python 3, map returns a map object, which is a generator. # Python 2.X >>> map(str, [1, 2, 3, 4, 5]) ['1', '2', '3', '4', '5'] >>> type(_) >>> # Python 3.X >>> map(str, [1, 2, 3, 4, 5])

>>> type(_)

# We need to apply map again because we "consumed" the previous map.... >>> map(str, [1, 2, 3, 4, 5]) >>> list(_) ['1', '2', '3', '4', '5']

In Python 2, you can pass None to serve as an identity function. This no longer works in Python 3. Python 2.x Version

≥ 2.3

>>> map(None, [0, 1, 2, 3, 0, 4]) [0, 1, 2, 3, 0, 4]

Python 3.x Version

≥ 3.0

>>> list(map(None, [0, 1, 2, 3, 0, 5])) Traceback (most recent call last): File "", line 1, in TypeError: 'NoneType' object is not callable

Moreover, when passing more than one iterable as argument in Python 2, map pads the shorter iterables with None (similar to itertools.izip_longest). In Python 3, iteration stops after the shortest iterable.

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In Python 2: Python 2.x Version

≥ 2.3

>>> map(None, [1, 2, 3], [1, 2], [1, 2, 3, 4, 5]) [(1, 1, 1), (2, 2, 2), (3, None, 3), (None, None, 4), (None, None, 5)]

In Python 3: Python 3.x Version

≥ 3.0

>>> list(map(lambda x, y, z: (x, y, z), [1, 2, 3], [1, 2], [1, 2, 3, 4, 5])) [(1, 1, 1), (2, 2, 2)] # to obtain the same padding as in Python 2 use zip_longest from itertools >>> import itertools >>> list(itertools.zip_longest([1, 2, 3], [1, 2], [1, 2, 3, 4, 5])) [(1, 1, 1), (2, 2, 2), (3, None, 3), (None, None, 4), (None, None, 5)]

Note: instead of map consider using list comprehensions, which are Python 2/3 compatible. Replacing map(str, [1, 2, 3, 4, 5]): >>> [str(i) for i in [1, 2, 3, 4, 5]] ['1', '2', '3', '4', '5']

Section 149.20: The round() function tie-breaking and return type round() tie breaking In Python 2, using round() on a number equally close to two integers will return the one furthest from 0. For example: Python 2.x Version

≤ 2.7

round(1.5) # Out: 2.0 round(0.5) # Out: 1.0 round(-0.5) # Out: -1.0 round(-1.5) # Out: -2.0

In Python 3 however, round() will return the even integer (aka bankers' rounding). For example: Python 3.x Version

≥ 3.0

round(1.5) # Out: 2 round(0.5) # Out: 0 round(-0.5) # Out: 0 round(-1.5) # Out: -2

The round() function follows the half to even rounding strategy that will round half-way numbers to the nearest even integer (for example, round(2.5) now returns 2 rather than 3.0). As per reference in Wikipedia, this is also known as unbiased rounding, convergent rounding, statistician's rounding, Dutch rounding, Gaussian rounding, or odd-even rounding. Half to even rounding is part of the IEEE 754 standard and it's also the default rounding mode in Microsoft's .NET. This rounding strategy tends to reduce the total rounding error. Since on average the amount of numbers that are rounded up is the same as the amount of numbers that are rounded down, rounding errors cancel out. Other GoalKicker.com – Python® Notes for Professionals

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rounding methods instead tend to have an upwards or downwards bias in the average error.

round() return type The round() function returns a float type in Python 2.7 Python 2.x Version

≤ 2.7

round(4.8) # 5.0

Starting from Python 3.0, if the second argument (number of digits) is omitted, it returns an int. Python 3.x Version

≥ 3.0

round(4.8) # 5

Section 149.21: File I/O file is no longer a builtin name in 3.x (open still works).

Internal details of file I/O have been moved to the standard library io module, which is also the new home of StringIO: import io assert io.open is open # the builtin is an alias buffer = io.StringIO() buffer.write('hello, ') # returns number of characters written buffer.write('world!\n') buffer.getvalue() # 'hello, world!\n'

The file mode (text vs binary) now determines the type of data produced by reading a file (and type required for writing): with open('data.txt') as f: first_line = next(f) assert type(first_line) is str with open('data.bin', 'rb') as f: first_kb = f.read(1024) assert type(first_kb) is bytes

The encoding for text files defaults to whatever is returned by locale.getpreferredencoding(False). To specify an encoding explicitly, use the encoding keyword parameter: with open('old_japanese_poetry.txt', 'shift_jis') as text: haiku = text.read()

Section 149.22: cmp function removed in Python 3 In Python 3 the cmp built-in function was removed, together with the __cmp__ special method. From the documentation: The cmp() function should be treated as gone, and the __cmp__() special method is no longer supported. GoalKicker.com – Python® Notes for Professionals

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Use __lt__() for sorting, __eq__() with __hash__(), and other rich comparisons as needed. (If you really need the cmp() functionality, you could use the expression (a > b) - (a < b) as the equivalent for cmp(a, b).)

Moreover all built-in functions that accepted the cmp parameter now only accept the key keyword only parameter. In the functools module there is also useful function cmp_to_key(func) that allows you to convert from a cmp-style function to a key-style function: Transform an old-style comparison function to a key function. Used with tools that accept key functions (such as sorted(), min(), max(), heapq.nlargest(), heapq.nsmallest(), itertools.groupby()). This function is primarily used as a transition tool for programs being converted from Python 2 which supported the use of comparison functions.

Section 149.23: Octal Constants In Python 2, an octal literal could be defined as >>> 0755 # only Python 2 To ensure cross-compatibility, use 0o755 # both Python 2 and Python 3

Section 149.24: Return value when writing to a file object In Python 2, writing directly to a file handle returns None: Python 2.x Version

≥ 2.3

hi = sys.stdout.write('hello world\\n') # Out: hello world type(hi) # Out:

In Python 3, writing to a handle will return the number of characters written when writing text, and the number of bytes written when writing bytes: Python 3.x Version

≥ 3.0

import sys char_count = sys.stdout.write('hello world ?\\n') # Out: hello world ? char_count # Out: 14 byte_count = sys.stdout.buffer.write(b'hello world \\xf0\\x9f\\x90\\x8d\\n') # Out: hello world ? byte_count # Out: 17

Section 149.25: exec statement is a function in Python 3 In Python 2, exec is a statement, with special syntax: exec code [in globals[, locals]]. In Python 3 exec is now GoalKicker.com – Python® Notes for Professionals

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a function: exec(code, [, globals[, locals]]), and the Python 2 syntax will raise a SyntaxError. As print was changed from statement into a function, a __future__ import was also added. However, there is no from __future__ import exec_function, as it is not needed: the exec statement in Python 2 can be also used with

syntax that looks exactly like the exec function invocation in Python 3. Thus you can change the statements Python 2.x Version

≥ 2.3

exec 'code' exec 'code' in global_vars exec 'code' in global_vars, local_vars

to forms Python 3.x Version

≥ 3.0

exec('code') exec('code', global_vars) exec('code', global_vars, local_vars)

and the latter forms are guaranteed to work identically in both Python 2 and Python 3.

Section 149.26: encode/decode to hex no longer available Python 2.x Version

≤ 2.7

"1deadbeef3".decode('hex') # Out: '\x1d\xea\xdb\xee\xf3' '\x1d\xea\xdb\xee\xf3'.encode('hex') # Out: 1deadbeef3

Python 3.x Version

≥ 3.0

"1deadbeef3".decode('hex') # Traceback (most recent call last): # File "", line 1, in # AttributeError: 'str' object has no attribute 'decode' b"1deadbeef3".decode('hex') # Traceback (most recent call last): # File "", line 1, in # LookupError: 'hex' is not a text encoding; use codecs.decode() to handle arbitrary codecs '\x1d\xea\xdb\xee\xf3'.encode('hex') # Traceback (most recent call last): # File "", line 1, in # LookupError: 'hex' is not a text encoding; use codecs.encode() to handle arbitrary codecs b'\x1d\xea\xdb\xee\xf3'.encode('hex') # Traceback (most recent call last): # File "", line 1, in # AttributeError: 'bytes' object has no attribute 'encode'

However, as suggested by the error message, you can use the codecs module to achieve the same result: import codecs codecs.decode('1deadbeef4', 'hex') # Out: b'\x1d\xea\xdb\xee\xf4' codecs.encode(b'\x1d\xea\xdb\xee\xf4', 'hex') # Out: b'1deadbeef4'

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codecs.encode(b'\x1d\xea\xdb\xee\xff', 'hex').decode('ascii') # Out: '1deadbeeff'

Section 149.27: Dictionary method changes In Python 3, many of the dictionary methods are quite different in behaviour from Python 2, and many were removed as well: has_key, iter* and view* are gone. Instead of d.has_key(key), which had been long deprecated, one must now use key in d. In Python 2, dictionary methods keys, values and items return lists. In Python 3 they return view objects instead; the view objects are not iterators, and they differ from them in two ways, namely: they have size (one can use the len function on them) they can be iterated over many times Additionally, like with iterators, the changes in the dictionary are reflected in the view objects. Python 2.7 has backported these methods from Python 3; they're available as viewkeys, viewvalues and viewitems. To transform Python 2 code to Python 3 code, the corresponding forms are: d.keys(), d.values() and d.items() of Python 2 should be changed to list(d.keys()), list(d.values())

and list(d.items()) d.iterkeys(), d.itervalues() and d.iteritems() should be changed to iter(d.keys()), or even better, iter(d); iter(d.values()) and iter(d.items()) respectively

and finally Python 2.7 method calls d.viewkeys(), d.viewvalues() and d.viewitems() can be replaced with d.keys(), d.values() and d.items().

Porting Python 2 code that iterates over dictionary keys, values or items while mutating it is sometimes tricky. Consider: d = {'a': 0, 'b': 1, 'c': 2, '!': 3} for key in d.keys(): if key.isalpha(): del d[key]

The code looks as if it would work similarly in Python 3, but there the keys method returns a view object, not a list, and if the dictionary changes size while being iterated over, the Python 3 code will crash with RuntimeError: dictionary changed size during iteration. The solution is of course to properly write for key in list(d).

Similarly, view objects behave differently from iterators: one cannot use next() on them, and one cannot resume iteration; it would instead restart; if Python 2 code passes the return value of d.iterkeys(), d.itervalues() or d.iteritems() to a method that expects an iterator instead of an iterable, then that should be iter(d), iter(d.values()) or iter(d.items()) in Python 3.

Section 149.28: Class Boolean Value Python 2.x Version

≤ 2.7

In Python 2, if you want to define a class boolean value by yourself, you need to implement the __nonzero__ method on your class. The value is True by default. class MyClass: def __nonzero__(self): return False

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my_instance = MyClass() print bool(MyClass) print bool(my_instance)

Python 3.x Version

# True # False

≥ 3.0

In Python 3, __bool__ is used instead of __nonzero__ class MyClass: def __bool__(self): return False my_instance = MyClass() print(bool(MyClass)) print(bool(my_instance))

# True # False

Section 149.29: hasattr function bug in Python 2 In Python 2, when a property raise an error, hasattr will ignore this property, returning False. class A(object): @property def get(self): raise IOError

class B(object): @property def get(self): return 'get in b' a = A() b = B() print 'a # output print 'b # output

hasattr get: ', hasattr(a, 'get') False in Python 2 (fixed, True in Python 3) hasattr get', hasattr(b, 'get') True in Python 2 and Python 3

This bug is fixed in Python3. So if you use Python 2, use try: a.get except AttributeError: print("no get property!")

or use getattr instead p = getattr(a, "get", None) if p is not None: print(p) else: print("no get property!")

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Chapter 150: 2to3 tool Parameter

Description 2to3 accepts a list of files or directories which is to be transformed as its argument. The directories are recursively traversed for Python sources.

filename / directory_name Option

Option Description

-f FIX, --fix=FIX

Specify transformations to be applied; default: all. List available transformations with --list-fixes

-j PROCESSES, --processes=PROCESSES

Run 2to3 concurrently

-x NOFIX, --nofix=NOFIX

Exclude a transformation

-l, --list-fixes

List available transformations

-p, --print-function

Change the grammar so that print() is considered a function

-v, --verbose

More verbose output

--no-diffs

Do not output diffs of the refactoring

-w

Write back modified files

-n, --nobackups

Do not create backups of modified files

-o OUTPUT_DIR, --outputdir=OUTPUT_DIR

Place output files in this directory instead of overwriting input files. Requires the -n flag, as backup files are unnecessary when the input files are not modified.

-W, --write-unchanged-files

Write output files even is no changes were required. Useful with -o so that a complete source tree is translated and copied. Implies -w.

--add-suffix=ADD_SUFFIX

Specify a string to be appended to all output filenames. Requires -n if non-empty. Ex.: --add-suffix='3' will generate .py3 files.

Section 150.1: Basic Usage Consider the following Python2.x code. Save the file as example.py Python 2.x Version

≥ 2.0

def greet(name): print "Hello, {0}!".format(name) print "What's your name?" name = raw_input() greet(name)

In the above file, there are several incompatible lines. The raw_input() method has been replaced with input() in Python 3.x and print is no longer a statement, but a function. This code can be converted to Python 3.x code using the 2to3 tool. Unix $ 2to3 example.py

Windows > path/to/2to3.py example.py

Running the above code will output the differences against the original source file as shown below. RefactoringTool: RefactoringTool: RefactoringTool: RefactoringTool:

Skipping Skipping Skipping Skipping

implicit implicit implicit implicit

fixer: fixer: fixer: fixer:

buffer idioms set_literal ws_comma

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RefactoringTool: Refactored example.py --- example.py (original) +++ example.py (refactored) @@ -1,5 +1,5 @@ def greet(name): print "Hello, {0}!".format(name) -print "What's your name?" -name = raw_input() + print("Hello, {0}!".format(name)) +print("What's your name?") +name = input() greet(name) RefactoringTool: Files that need to be modified: RefactoringTool: example.py

The modifications can be written back to the source file using the -w flag. A backup of the original file called example.py.bak is created, unless the -n flag is given.

Unix $ 2to3 -w example.py

Windows > path/to/2to3.py -w example.py

Now the example.py file has been converted from Python 2.x to Python 3.x code. Once finished, example.py will contain the following valid Python3.x code: Python 3.x Version

≥ 3.0

def greet(name): print("Hello, {0}!".format(name)) print("What's your name?") name = input() greet(name)

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Chapter 151: Non-ocial Python implementations Section 151.1: IronPython Open-source implementation for .NET and Mono written in C#, licensed under Apache License 2.0. It relies on DLR (Dynamic Language Runtime). It supports only version 2.7, version 3 is currently being developed. Differences with CPython: Tight integration with .NET Framework. Strings are Unicode by default. Does not support extensions for CPython written in C. Does not suffer from Global Interpreter Lock. Performance is usually lower, though it depends on tests. Hello World print "Hello World!"

You can also use .NET functions: import clr from System import Console Console.WriteLine("Hello World!")

External links Official website GitHub repository

Section 151.2: Jython Open-source implementation for JVM written in Java, licensed under Python Software Foundation License. It supports only version 2.7, version 3 is currently being developed. Differences with CPython: Tight integration with JVM. Strings are Unicode. Does not support extensions for CPython written in C. Does not suffer from Global Interpreter Lock. Performance is usually lower, though it depends on tests. Hello World print "Hello World!"

You can also use Java functions: from java.lang import System System.out.println("Hello World!")

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Section 151.3: Transcrypt Transcrypt is a tool to precompile a fairly extensive subset of Python into compact, readable Javascript. It has the following characteristics: Allows for classical OO programming with multiple inheritance using pure Python syntax, parsed by CPython’s native parser Seamless integration with the universe of high-quality web-oriented JavaScript libraries, rather than the desktop-oriented Python ones Hierarchical URL based module system allowing module distribution via PyPi Simple relation between Python source and generated JavaScript code for easy debugging Multi-level sourcemaps and optional annotation of target code with source references Compact downloads, kB’s rather than MB’s Optimized JavaScript code, using memoization (call caching) to optionally bypass the prototype lookup chain Operator overloading can be switched on and off locally to facilitate readable numerical math Code size and speed Experience has shown that 650 kB of Python sourcecode roughly translates in the same amount of JavaScript source code. The speed matches the speed of handwritten JavaScript and can surpass it if call memoizing is switched on. Integration with HTML

Hello demo

... Click me repeatedly!

... And click me repeatedly too!

Integration with JavaScript and DOM from itertools import chain class SolarSystem: planets = [list (chain (planet, (index + 1,))) for index, planet in enumerate (( ('Mercury', 'hot', 2240), ('Venus', 'sulphurous', 6052), ('Earth', 'fertile', 6378), ('Mars', 'reddish', 3397), ('Jupiter', 'stormy', 71492), ('Saturn', 'ringed', 60268), ('Uranus', 'cold', 25559), ('Neptune', 'very cold', 24766) ))] lines = ( '{} is a {} planet', 'The radius of {} is {} km', '{} is planet nr. {} counting from the sun' ) def __init__ (self): self.lineIndex = 0

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def greet (self): self.planet = self.planets [int (Math.random () * len (self.planets))] document.getElementById ('greet') .innerHTML = 'Hello {}'.format (self.planet [0]) self.explain () def explain (self): document.getElementById ('explain').innerHTML = ( self.lines [self.lineIndex] .format (self.planet [0], self.planet [self.lineIndex + 1]) ) self.lineIndex = (self.lineIndex + 1) % 3 solarSystem = SolarSystem ()

Integration with other JavaScript libraries Transcrypt can be used in combination with any JavaScript library without special measures or syntax. In the documentation examples are given for a.o. react.js, riot.js, fabric.js and node.js. Relation between Python and JavaScript code Python class A: def __init__ (self, x): self.x = x def show (self, label): print ('A.show', label, self.x) class B: def __init__ (self, y): alert ('In B constructor') self.y = y def show (self, label): print ('B.show', label, self.y) class C (A, B): def __init__ (self, x, y): alert ('In C constructor') A.__init__ (self, x) B.__init__ (self, y) self.show ('constructor') def show (self, label): B.show (self, label) print ('C.show', label, self.x, self.y) a = A (1001) a.show ('america') b = B (2002) b.show ('russia') c = C (3003, 4004) c.show ('netherlands') show2 = c.show show2 ('copy')

JavaScript

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var A = __class__ ('A', [object], { get __init__ () {return __get__ (this, function (self, x) self.x = x; });}, get show () {return __get__ (this, function (self, label) print ('A.show', label, self.x); });} }); var B = __class__ ('B', [object], { get __init__ () {return __get__ (this, function (self, y) alert ('In B constructor'); self.y = y; });}, get show () {return __get__ (this, function (self, label) print ('B.show', label, self.y); });} }); var C = __class__ ('C', [A, B], { get __init__ () {return __get__ (this, function (self, x, alert ('In C constructor'); A.__init__ (self, x); B.__init__ (self, y); self.show ('constructor'); });}, get show () {return __get__ (this, function (self, label) B.show (self, label); print ('C.show', label, self.x, self.y); });} }); var a = A (1001); a.show ('america'); var b = B (2002); b.show ('russia'); var c = C (3003, 4004); c.show ('netherlands'); var show2 = c.show; show2 ('copy');

{

{

{

{

y) {

{

External links Official website: http://www.transcrypt.org/ Repository: https://github.com/JdeH/Transcrypt

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Chapter 152: Abstract syntax tree Section 152.1: Analyze functions in a python script This analyzes a python script and, for each defined function, reports the line number where the function began, where the signature ends, where the docstring ends, and where the function definition ends. #!/usr/local/bin/python3 import ast import sys """ The data we collect. Each key is a function name; each value is a dict with keys: firstline, sigend, docend, and lastline and values of line numbers where that happens. """ functions = {} def process(functions): """ Handle the function data stored in functions. """ for funcname,data in functions.items(): print("function:",funcname) print("\tstarts at line:",data['firstline']) print("\tsignature ends at line:",data['sigend']) if ( data['sigend'] < data['docend'] ): print("\tdocstring ends at line:",data['docend']) else: print("\tno docstring") print("\tfunction ends at line:",data['lastline']) print() class FuncLister(ast.NodeVisitor): def visit_FunctionDef(self, node): """ Recursively visit all functions, determining where each function starts, where its signature ends, where the docstring ends, and where the function ends. """ functions[node.name] = {'firstline':node.lineno} sigend = max(node.lineno,lastline(node.args)) functions[node.name]['sigend'] = sigend docstring = ast.get_docstring(node) docstringlength = len(docstring.split('\n')) if docstring else -1 functions[node.name]['docend'] = sigend+docstringlength functions[node.name]['lastline'] = lastline(node) self.generic_visit(node) def lastline(node): """ Recursively find the last line of a node """ return max( [ node.lineno if hasattr(node,'lineno') else -1 , ] +[lastline(child) for child in ast.iter_child_nodes(node)] ) def readin(pythonfilename): """ Read the file name and store the function data into functions. """ with open(pythonfilename) as f: code = f.read() FuncLister().visit(ast.parse(code)) def analyze(file,process): """ Read the file and process the function data. """ readin(file) process(functions)

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if __name__ == '__main__': if len(sys.argv)>1: for file in sys.argv[1:]: analyze(file,process) else: analyze(sys.argv[0],process)

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Chapter 153: Unicode and bytes Parameter Details encoding The encoding to use, e.g. 'ascii', 'utf8', etc... errors

The errors mode, e.g. 'replace' to replace bad characters with question marks, 'ignore' to ignore bad characters, etc...

Section 153.1: Encoding/decoding error handling .encode and .decode both have error modes.

The default is 'strict', which raises exceptions on error. Other modes are more forgiving. Encoding >>> "£13.55".encode('ascii', b'?13.55' >>> "£13.55".encode('ascii', b'13.55' >>> "£13.55".encode('ascii', b'\\N{POUND SIGN}13.55' >>> "£13.55".encode('ascii', b'£13.55' >>> "£13.55".encode('ascii', b'\\xa313.55'

errors='replace') errors='ignore') errors='namereplace') errors='xmlcharrefreplace') errors='backslashreplace')

Decoding >>> b = "£13.55".encode('utf8') >>> b.decode('ascii', errors='replace') '��13.55' >>> b.decode('ascii', errors='ignore') '13.55' >>> b.decode('ascii', errors='backslashreplace') '\\xc2\\xa313.55'

Morale It is clear from the above that it is vital to keep your encodings straight when dealing with unicode and bytes.

Section 153.2: File I/O Files opened in a non-binary mode (e.g. 'r' or 'w') deal with strings. The default encoding is 'utf8'. open(fn, mode='r') open(fn, mode='r', encoding='utf16')

# opens file for reading in utf8 # opens file for reading utf16

# ERROR: cannot write bytes when a string is expected: open("foo.txt", "w").write(b"foo")

Files opened in a binary mode (e.g. 'rb' or 'wb') deal with bytes. No encoding argument can be specified as there is no encoding. open(fn, mode='wb')

# open file for writing bytes

# ERROR: cannot write string when bytes is expected: open(fn, mode='wb').write("hi")

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Section 153.3: Basics In Python 3 str is the type for unicode-enabled strings, while bytes is the type for sequences of raw bytes. type("f") == type(u"f") type(b"f")

# True, #

In Python 2 a casual string was a sequence of raw bytes by default and the unicode string was every string with "u" prefix. type("f") == type(b"f") type(u"f")

# True, #

Unicode to bytes Unicode strings can be converted to bytes with .encode(encoding). Python 3 >>> "£13.55".encode('utf8') b'\xc2\xa313.55' >>> "£13.55".encode('utf16') b'\xff\xfe\xa3\x001\x003\x00.\x005\x005\x00'

Python 2 in py2 the default console encoding is sys.getdefaultencoding() == 'ascii' and not utf-8 as in py3, therefore printing it as in the previous example is not directly possible. >>> print type(u"£13.55".encode('utf8'))

>>> print u"£13.55".encode('utf8') SyntaxError: Non-ASCII character '\xc2' in... # with encoding set inside a file # -*- coding: utf-8 -*>>> print u"£13.55".encode('utf8') ┬ú13.55

If the encoding can't handle the string, a `UnicodeEncodeError` is raised: >>> "£13.55".encode('ascii') Traceback (most recent call last): File "", line 1, in UnicodeEncodeError: 'ascii' codec can't encode character '\xa3' in position 0: ordinal not in range(128)

Bytes to unicode Bytes can be converted to unicode strings with .decode(encoding). A sequence of bytes can only be converted into a unicode string via the appropriate encoding! >>> b'\xc2\xa313.55'.decode('utf8')

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'£13.55'

If the encoding can't handle the string, a UnicodeDecodeError is raised: >>> b'\xc2\xa313.55'.decode('utf16') Traceback (most recent call last): File "", line 1, in File "/Users/csaftoiu/csaftoiu-github/yahoo-groupsbackup/.virtualenv/bin/../lib/python3.5/encodings/utf_16.py", line 16, in decode return codecs.utf_16_decode(input, errors, True) UnicodeDecodeError: 'utf-16-le' codec can't decode byte 0x35 in position 6: truncated data

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Chapter 154: Python Serial Communication (pyserial) parameter details port Device name e.g. /dev/ttyUSB0 on GNU/Linux or COM3 on Windows. baudrate

baudrate type: int default: 9600 standard values: 50, 75, 110, 134, 150, 200, 300, 600, 1200, 1800, 2400, 4800, 9600, 19200, 38400, 57600, 115200

Section 154.1: Initialize serial device import serial #Serial takes these two parameters: serial device and baudrate ser = serial.Serial('/dev/ttyUSB0', 9600)

Section 154.2: Read from serial port Initialize serial device import serial #Serial takes two parameters: serial device and baudrate ser = serial.Serial('/dev/ttyUSB0', 9600)

to read single byte from serial device data = ser.read()

to read given number of bytes from the serial device data = ser.read(size=5)

to read one line from serial device. data = ser.readline()

to read the data from serial device while something is being written over it. #for python2.7 data = ser.read(ser.inWaiting()) #for python3 ser.read(ser.inWaiting)

Section 154.3: Check what serial ports are available on your machine To get a list of available serial ports use python -m serial.tools.list_ports

at a command prompt or from serial.tools import list_ports

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list_ports.comports()

# Outputs list of available serial ports

from the Python shell.

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VIDEO: Machine Learning A-Z: Hands-On Python In Data Science Learn to create Machine Learning Algorithms in Python from two Data Science experts. Code templates included.

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Chapter 155: Neo4j and Cypher using Py2Neo Section 155.1: Adding Nodes to Neo4j Graph results = News.objects.todays_news() for r in results: article = graph.merge_one("NewsArticle", "news_id", r) article.properties["title"] = results[r]['news_title'] article.properties["timestamp"] = results[r]['news_timestamp'] article.push() [...]

Adding nodes to the graph is pretty simple,graph.merge_one is important as it prevents duplicate items. (If you run the script twice, then the second time it would update the title and not create new nodes for the same articles) timestamp should be an integer and not a date string as neo4j doesn't really have a date datatype. This causes

sorting issues when you store date as '05-06-1989' article.push() is an the call that actually commits the operation into neo4j. Don't forget this step.

Section 155.2: Importing and Authenticating from py2neo import authenticate, Graph, Node, Relationship authenticate("localhost:7474", "neo4j", "") graph = Graph()

You have to make sure your Neo4j Database exists at localhost:7474 with the appropriate credentials. the graph object is your interface to the neo4j instance in the rest of your python code. Rather than making this a global variable, you should keep it in a class's __init__ method.

Section 155.3: Adding Relationships to Neo4j Graph results = News.objects.todays_news() for r in results: article = graph.merge_one("NewsArticle", "news_id", r) if 'LOCATION' in results[r].keys(): for loc in results[r]['LOCATION']: loc = graph.merge_one("Location", "name", loc) try: rel = graph.create_unique(Relationship(article, "about_place", loc)) except Exception, e: print e create_unique is important for avoiding duplicates. But otherwise it's a pretty straightforward operation. The

relationship name is also important as you would use it in advanced cases.

Section 155.4: Query 1 : Autocomplete on News Titles def get_autocomplete(text): query = """ start n = node(*) where n.name =~ '(?i)%s.*' return n.name,labels(n) limit 10; """

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query = query % (text) obj = [] for res in graph.cypher.execute(query): # print res[0],res[1] obj.append({'name':res[0],'entity_type':res[1]}) return res

This is a sample cypher query to get all nodes with the property name that starts with the argument text.

Section 155.5: Query 2 : Get News Articles by Location on a particular date def search_news_by_entity(location,timestamp): query = """ MATCH (n)-[]->(l) where l.name='%s' and n.timestamp='%s' RETURN n.news_id limit 10 """ query = query % (location,timestamp) news_ids = [] for res in graph.cypher.execute(query): news_ids.append(str(res[0])) return news_ids

You can use this query to find all news articles (n) connected to a location (l) by a relationship.

Section 155.6: Cypher Query Samples Count articles connected to a particular person over time MATCH (n)-[]->(l) where l.name='Donald Trump' RETURN n.date,count(*) order by n.date

Search for other People / Locations connected to the same news articles as Trump with at least 5 total relationship nodes. MATCH (n:NewsArticle)-[]->(l) where l.name='Donald Trump' MATCH (n:NewsArticle)-[]->(m) with m,count(n) as num where num>5 return labels(m)[0],(m.name), num order by num desc limit 10

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Chapter 156: Basic Curses with Python Section 156.1: The wrapper() helper function While the basic invocation above is easy enough, the curses package provides the wrapper(func, ...) helper function. The example below contains the equivalent of above: main(scr, *args): # -- Perform an action with Screen -scr.border(0) scr.addstr(5, 5, 'Hello from Curses!', curses.A_BOLD) scr.addstr(6, 5, 'Press q to close this screen', curses.A_NORMAL) while True: # stay in this loop till the user presses 'q' ch = scr.getch() if ch == ord('q'): curses.wrapper(main)

Here, wrapper will initialize curses, create stdscr, a WindowObject and pass both stdscr, and any further arguments to func. When func returns, wrapper will restore the terminal before the program exits.

Section 156.2: Basic Invocation Example import curses import traceback try: # -- Initialize -stdscr = curses.initscr() curses.noecho() curses.cbreak() stdscr.keypad(1)

# # # # #

initialize curses screen turn off auto echoing of keypress on to screen enter break mode where pressing Enter key after keystroke is not required for it to register enable special Key values such as curses.KEY_LEFT etc

# -- Perform an action with Screen -stdscr.border(0) stdscr.addstr(5, 5, 'Hello from Curses!', curses.A_BOLD) stdscr.addstr(6, 5, 'Press q to close this screen', curses.A_NORMAL) while True: # stay in this loop till the user presses 'q' ch = stdscr.getch() if ch == ord('q'): break # -- End of user code -except: traceback.print_exc()

# print trace back log of the error

finally: # --- Cleanup on exit --stdscr.keypad(0) curses.echo() curses.nocbreak() curses.endwin()

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Chapter 157: Templates in python Section 157.1: Simple data output program using template from string import Template data = dict(item = "candy", price = 8, qty = 2) # define the template t = Template("Simon bought $qty $item for $price dollar") print(t.substitute(data))

Output: Simon bought 2 candy for 8 dollar

Templates support $-based substitutions instead of %-based substitution. Substitute (mapping, keywords) performs template substitution, returning a new string. Mapping is any dictionary-like object with keys that match with the template placeholders. In this example, price and qty are placeholders. Keyword arguments can also be used as placeholders. Placeholders from keywords take precedence if both are present.

Section 157.2: Changing delimiter You can change the "$" delimiter to any other. The following example: from string import Template class MyOtherTemplate(Template): delimiter = "#"

data = dict(id = 1, name = "Ricardo") t = MyOtherTemplate("My name is #name and I have the id: #id") print(t.substitute(data))

You can read de docs here

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Chapter 158: Pillow Section 158.1: Read Image File from PIL import Image im = Image.open("Image.bmp")

Section 158.2: Convert files to JPEG from __future__ import print_function import os, sys from PIL import Image for infile in sys.argv[1:]: f, e = os.path.splitext(infile) outfile = f + ".jpg" if infile != outfile: try: Image.open(infile).save(outfile) except IOError: print("cannot convert", infile)

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Chapter 159: The pass statement Section 159.1: Ignore an exception try: metadata = metadata['properties'] except KeyError: pass

Section 159.2: Create a new Exception that can be caught class CompileError(Exception): pass

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VIDEO: Machine Learning, Data Science and Deep Learning with Python Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks

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Chapter 160: CLI subcommands with precise help output Different ways to create subcommands like in hg or svn with the exact command line interface and help output as shown in Remarks section. Parsing Command Line arguments covers broader topic of arguments parsing.

Section 160.1: Native way (no libraries) """ usage: sub commands: status list """

show status print list

import sys def check(): print("status") return 0 if sys.argv[1:] == ['status']: sys.exit(check()) elif sys.argv[1:] == ['list']: print("list") else: print(__doc__.strip())

Output without arguments: usage: sub commands: status - show status list - print list

Pros: no deps everybody should be able to read that complete control over help formatting

Section 160.2: argparse (default help formatter) import argparse import sys def check(): print("status") return 0

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parser = argparse.ArgumentParser(prog="sub", add_help=False) subparser = parser.add_subparsers(dest="cmd") subparser.add_parser('status', help='show status') subparser.add_parser('list', help='print list') # hack to show help when no arguments supplied if len(sys.argv) == 1: parser.print_help() sys.exit(0) args = parser.parse_args() if args.cmd == 'list': print('list') elif args.cmd == 'status': sys.exit(check())

Output without arguments: usage: sub {status,list} ... positional arguments: {status,list} status show status list print list

Pros: comes with Python option parsing is included

Section 160.3: argparse (custom help formatter) import argparse import sys class CustomHelpFormatter(argparse.HelpFormatter): def _format_action(self, action): if type(action) == argparse._SubParsersAction: # inject new class variable for subcommand formatting subactions = action._get_subactions() invocations = [self._format_action_invocation(a) for a in subactions] self._subcommand_max_length = max(len(i) for i in invocations) if type(action) == argparse._SubParsersAction._ChoicesPseudoAction: # format subcommand help line subcommand = self._format_action_invocation(action) # type: str width = self._subcommand_max_length help_text = "" if action.help: help_text = self._expand_help(action) return " {:{width}} - {}\n".format(subcommand, help_text, width=width) elif type(action) == argparse._SubParsersAction: # process subcommand help section msg = '\n' for subaction in action._get_subactions(): msg += self._format_action(subaction) return msg

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else: return super(CustomHelpFormatter, self)._format_action(action)

def check(): print("status") return 0 parser = argparse.ArgumentParser(usage="sub ", add_help=False, formatter_class=CustomHelpFormatter) subparser = parser.add_subparsers(dest="cmd") subparser.add_parser('status', help='show status') subparser.add_parser('list', help='print list') # custom help message parser._positionals.title = "commands" # hack to show help when no arguments supplied if len(sys.argv) == 1: parser.print_help() sys.exit(0) args = parser.parse_args() if args.cmd == 'list': print('list') elif args.cmd == 'status': sys.exit(check())

Output without arguments: usage: sub commands: status list -

show status print list

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Chapter 161: Database Access Section 161.1: SQLite SQLite is a lightweight, disk-based database. Since it does not require a separate database server, it is often used for prototyping or for small applications that are often used by a single user or by one user at a given time. import sqlite3 conn = sqlite3.connect("users.db") c = conn.cursor() c.execute("CREATE TABLE user (name text, age integer)") c.execute("INSERT INTO user VALUES ('User A', 42)") c.execute("INSERT INTO user VALUES ('User B', 43)") conn.commit() c.execute("SELECT * FROM user") print(c.fetchall()) conn.close()

The code above connects to the database stored in the file named users.db, creating the file first if it doesn't already exist. You can interact with the database via SQL statements. The result of this example should be: [(u'User A', 42), (u'User B', 43)]

The SQLite Syntax: An in-depth analysis Getting started 1. Import the sqlite module using >>> import sqlite3

2. To use the module, you must first create a Connection object that represents the database. Here the data will be stored in the example.db file: >>> conn = sqlite3.connect('users.db')

Alternatively, you can also supply the special name :memory: to create a temporary database in RAM, as follows: >>> conn = sqlite3.connect(':memory:')

3. Once you have a Connection, you can create a Cursor object and call its execute() method to perform SQL commands:

c = conn.cursor()

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# Create table c.execute('''CREATE TABLE stocks (date text, trans text, symbol text, qty real, price real)''') # Insert a row of data c.execute("INSERT INTO stocks VALUES ('2006-01-05','BUY','RHAT',100,35.14)") # Save (commit) the changes conn.commit() # We can also close the connection if we are done with it. # Just be sure any changes have been committed or they will be lost. conn.close()

Important Attributes and Functions of Connection 1. isolation_level It is an attribute used to get or set the current isolation level. None for autocommit mode or one of DEFERRED, IMMEDIATE or EXCLUSIVE.

2. cursor The cursor object is used to execute SQL commands and queries.

3. commit() Commits the current transaction.

4. rollback() Rolls back any changes made since the previous call to commit()

5. close() Closes the database connection. It does not call commit() automatically. If close() is called without first calling commit() (assuming you are not in autocommit mode) then all changes made will be lost.

6. total_changes An attribute that logs the total number of rows modified, deleted or inserted since the database was opened. 7. execute, executemany, and executescript These functions perform the same way as those of the cursor object. This is a shortcut since calling these functions through the connection object results in the creation of an intermediate cursor object and calls the corresponding method of the cursor object GoalKicker.com – Python® Notes for Professionals

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8. row_factory You can change this attribute to a callable that accepts the cursor and the original row as a tuple and will return the real result row. def dict_factory(cursor, row): d = {} for i, col in enumerate(cursor.description): d[col[0]] = row[i] return d conn = sqlite3.connect(":memory:") conn.row_factory = dict_factory

Important Functions of Cursor 1. execute(sql[, parameters]) Executes a single SQL statement. The SQL statement may be parametrized (i. e. placeholders instead of SQL literals). The sqlite3 module supports two kinds of placeholders: question marks ? (“qmark style”) and named placeholders :name (“named style”). import sqlite3 conn = sqlite3.connect(":memory:") cur = conn.cursor() cur.execute("create table people (name, age)") who = "Sophia" age = 37 # This IS the qmark STYLE: cur.execute("insert into people values (?, ?)", (who, age)) # AND this IS the named STYLE: cur.execute("select * from people where name=:who and age=:age", {"who": who, "age": age}) # the KEYS correspond TO the placeholders IN SQL print(cur.fetchone())

Beware: don't use %s for inserting strings into SQL commands as it can make your program vulnerable to an SQL injection attack (see SQL Injection ). 2. executemany(sql, seq_of_parameters) Executes an SQL command against all parameter sequences or mappings found in the sequence sql. The sqlite3 module also allows using an iterator yielding parameters instead of a sequence. L = [(1, 'abcd', 'dfj', 300), # A list OF tuples TO be inserted INTO the DATABASE (2, 'cfgd', 'dyfj', 400), (3, 'sdd', 'dfjh', 300.50)] conn = sqlite3.connect("test1.db") conn.execute("create table if not exists book (id int, name text, author text, price real)") conn.executemany("insert into book values (?, ?, ?, ?)", L)

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FOR ROW IN conn.execute("select * from book"): print(ROW)

You can also pass iterator objects as a parameter to executemany, and the function will iterate over the each tuple of values that the iterator returns. The iterator must return a tuple of values. import sqlite3 class IterChars: def __init__(SELF): SELF.count = ord('a') def __iter__(SELF): RETURN SELF def __next__(SELF): # (USE NEXT(SELF) FOR Python 2) IF SELF.count > ord('z'): raise StopIteration SELF.count += 1 RETURN (chr(SELF.count - 1),) conn = sqlite3.connect("abc.db") cur = conn.cursor() cur.execute("create table characters(c)") theIter = IterChars() cur.executemany("insert into characters(c) values (?)", theIter) ROWS = cur.execute("select c from characters") FOR ROW IN ROWS: print(ROW[0]),

3. executescript(sql_script) This is a nonstandard convenience method for executing multiple SQL statements at once. It issues a COMMIT statement first, then executes the SQL script it gets as a parameter. sql_script can be an instance of str or bytes. import sqlite3 conn = sqlite3.connect(":memory:") cur = conn.cursor() cur.executescript(""" create table person( firstname, lastname, age ); create table book( title, author, published ); insert into book(title, author, published) values ( 'Dirk Gently''s Holistic Detective Agency', 'Douglas Adams', 1987

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); """)

The next set of functions are used in conjunction with SELECT statements in SQL. To retrieve data after executing a SELECT statement, you can either treat the cursor as an iterator, call the cursor’s fetchone() method to retrieve a single matching row, or call fetchall() to get a list of the matching rows. Example of the iterator form: import sqlite3 stocks = [('2006-01-05', 'BUY', 'RHAT', 100, 35.14), ('2006-03-28', 'BUY', 'IBM', 1000, 45.0), ('2006-04-06', 'SELL', 'IBM', 500, 53.0), ('2006-04-05', 'BUY', 'MSFT', 1000, 72.0)] conn = sqlite3.connect(":memory:") conn.execute("create table stocks (date text, buysell text, symb text, amount int, price real)") conn.executemany("insert into stocks values (?, ?, ?, ?, ?)", stocks) cur = conn.cursor() FOR ROW IN cur.execute('SELECT * FROM stocks ORDER BY price'): print(ROW) # # # # #

Output: ('2006-01-05', ('2006-03-28', ('2006-04-06', ('2006-04-05',

'BUY', 'RHAT', 100, 35.14) 'BUY', 'IBM', 1000, 45.0) 'SELL', 'IBM', 500, 53.0) 'BUY', 'MSFT', 1000, 72.0)

4. fetchone() Fetches the next row of a query result set, returning a single sequence, or None when no more data is available. cur.execute('SELECT * FROM stocks ORDER BY price') i = cur.fetchone() while(i): print(i) i = cur.fetchone() # # # # #

Output: ('2006-01-05', ('2006-03-28', ('2006-04-06', ('2006-04-05',

'BUY', 'RHAT', 100, 35.14) 'BUY', 'IBM', 1000, 45.0) 'SELL', 'IBM', 500, 53.0) 'BUY', 'MSFT', 1000, 72.0)

5. fetchmany(size=cursor.arraysize) Fetches the next set of rows of a query result (specified by size), returning a list. If size is omitted, fetchmany returns a single row. An empty list is returned when no more rows are available. cur.execute('SELECT * FROM stocks ORDER BY price') print(cur.fetchmany(2)) # Output: # [('2006-01-05', 'BUY', 'RHAT', 100, 35.14), ('2006-03-28', 'BUY', 'IBM', 1000, 45.0)]

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6. fetchall() Fetches all (remaining) rows of a query result, returning a list. cur.execute('SELECT * FROM stocks ORDER BY price') print(cur.fetchall()) # Output: # [('2006-01-05', 'BUY', 'RHAT', 100, 35.14), ('2006-03-28', 'BUY', 'IBM', 1000, 45.0), ('2006-04-06', 'SELL', 'IBM', 500, 53.0), ('2006-04-05', 'BUY', 'MSFT', 1000, 72.0)]

SQLite and Python data types SQLite natively supports the following types: NULL, INTEGER, REAL, TEXT, BLOB. This is how the data types are converted when moving from SQL to Python or vice versa. None int float str bytes





NULL INTEGER/INT REAL/FLOAT TEXT/VARCHAR(n) BLOB

Section 161.2: Accessing MySQL database using MySQLdb The first thing you need to do is create a connection to the database using the connect method. After that, you will need a cursor that will operate with that connection. Use the execute method of the cursor to interact with the database, and every once in a while, commit the changes using the commit method of the connection object. Once everything is done, don't forget to close the cursor and the connection. Here is a Dbconnect class with everything you'll need. import MySQLdb class Dbconnect(object): def __init__(self): self.dbconection = MySQLdb.connect(host='host_example', port=int('port_example'), user='user_example', passwd='pass_example', db='schema_example') self.dbcursor = self.dbconection.cursor() def commit_db(self): self.dbconection.commit() def close_db(self): self.dbcursor.close() self.dbconection.close()

Interacting with the database is simple. After creating the object, just use the execute method.

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db = Dbconnect() db.dbcursor.execute('SELECT * FROM %s' % 'table_example')

If you want to call a stored procedure, use the following syntax. Note that the parameters list is optional. db = Dbconnect() db.callproc('stored_procedure_name', [parameters] )

After the query is done, you can access the results multiple ways. The cursor object is a generator that can fetch all the results or be looped. results = db.dbcursor.fetchall() for individual_row in results: first_field = individual_row[0]

If you want a loop using directly the generator: for individual_row in db.dbcursor: first_field = individual_row[0]

If you want to commit changes to the database: db.commit_db()

If you want to close the cursor and the connection: db.close_db()

Section 161.3: Connection Creating a connection According to PEP 249, the connection to a database should be established using a connect() constructor, which returns a Connection object. The arguments for this constructor are database dependent. Refer to the database specific topics for the relevant arguments. import MyDBAPI con = MyDBAPI.connect(*database_dependent_args)

This connection object has four methods: 1: close con.close()

Closes the connection instantly. Note that the connection is automatically closed if the Connection.__del___ method is called. Any pending transactions will implicitly be rolled back. 2: commit con.commit()

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3: rollback con.rollback()

Rolls back to the start of any pending transaction. In other words: this cancels any non-committed transaction to the database. 4: cursor cur = con.cursor()

Returns a Cursor object. This is used to do transactions on the database.

Section 161.4: PostgreSQL Database access using psycopg2 psycopg2 is the most popular PostgreSQL database adapter that is both lightweight and efficient. It is the current implementation of the PostgreSQL adapter. Its main features are the complete implementation of the Python DB API 2.0 specification and the thread safety (several threads can share the same connection) Establishing a connection to the database and creating a table import psycopg2 # Establish a connection to the database. # Replace parameter values with database credentials. conn = psycopg2.connect(database="testpython", user="postgres", host="localhost", password="abc123", port="5432") # Create a cursor. The cursor allows you to execute database queries. cur = conn.cursor() # Create a table. Initialise the table name, the column names and data type. cur.execute("""CREATE TABLE FRUITS ( id INT , fruit_name TEXT, color TEXT, price REAL )""") conn.commit() conn.close()

Inserting data into the table: # After creating the table as shown above, insert values into it. cur.execute("""INSERT INTO FRUITS (id, fruit_name, color, price) VALUES (1, 'Apples', 'green', 1.00)""") cur.execute("""INSERT INTO FRUITS (id, fruit_name, color, price) VALUES (1, 'Bananas', 'yellow', 0.80)""")

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# Set up a query and execute it cur.execute("""SELECT id, fruit_name, color, price FROM fruits""") # Fetch the data rows = cur.fetchall() # Do stuff with the data for row in rows: print "ID = {} ".format(row[0]) print "FRUIT NAME = {}".format(row[1]) print("COLOR = {}".format(row[2])) print("PRICE = {}".format(row[3]))

The output of the above would be: ID = 1 NAME = Apples COLOR = green PRICE = 1.0 ID = 2 NAME = Bananas COLOR = yellow PRICE = 0.8

And so, there you go, you now know half of all you need to know about psycopg2! :)

Section 161.5: Oracle database Pre-requisites: cx_Oracle package - See here for all versions Oracle instant client - For Windows x64, Linux x64 Setup: Install the cx_Oracle package as: sudo rpm -i

Extract the Oracle instant client and set environment variables as: ORACLE_HOME= PATH=$ORACLE_HOME:$PATH LD_LIBRARY_PATH=:$LD_LIBRARY_PATH

Creating a connection: import cx_Oracle class OraExec(object): _db_connection = None _db_cur = None def __init__(self):

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self._db_connection = cx_Oracle.connect('/@:/') self._db_cur = self._db_connection.cursor()

Get database version: ver = con.version.split(".") print ver

Sample Output: ['12', '1', '0', '2', '0'] Execute query: SELECT _db_cur.execute("select * from employees order by emp_id") for result in _db_cur: print result

Output will be in Python tuples: (10, 'SYSADMIN', 'IT-INFRA', 7) (23, 'HR ASSOCIATE', 'HUMAN RESOURCES', 6) Execute query: INSERT _db_cur.execute("insert into employees(emp_id, title, dept, grade) values (31, 'MTS', 'ENGINEERING', 7) _db_connection.commit()

When you perform insert/update/delete operations in an Oracle Database, the changes are only available within your session until commit is issued. When the updated data is committed to the database, it is then available to other users and sessions. Execute query: INSERT using Bind variables Reference Bind variables enable you to re-execute statements with new values, without the overhead of re-parsing the statement. Bind variables improve code re-usability, and can reduce the risk of SQL Injection attacks. rows = [ (1, "First" ), (2, "Second" ), (3, "Third" ) ] _db_cur.bindarraysize = 3 _db_cur.setinputsizes(int, 10) _db_cur.executemany("insert into mytab(id, data) values (:1, :2)", rows) _db_connection.commit()

Close connection: _db_connection.close()

The close() method closes the connection. Any connections not explicitly closed will be automatically released when the script ends.

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Section 161.6: Using sqlalchemy To use sqlalchemy for database: from sqlalchemy import create_engine from sqlalchemy.engine.url import URL

url = URL(drivername='mysql', username='user', password='passwd', host='host', database='db') engine = create_engine(url)

# sqlalchemy engine

Now this engine can be used: e.g. with pandas to fetch dataframes directly from mysql import pandas as pd con = engine.connect() dataframe = pd.read_sql(sql=query, con=con)

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Chapter 162: Connecting Python to SQL Server Section 162.1: Connect to Server, Create Table, Query Data Install the package: $ pip install pymssql import pymssql SERVER = "servername" USER = "username" PASSWORD = "password" DATABASE = "dbname" connection = pymssql.connect(server=SERVER, user=USER, password=PASSWORD, database=DATABASE) cursor = connection.cursor() # to access field as dictionary use cursor(as_dict=True) cursor.execute("SELECT TOP 1 * FROM TableName") row = cursor.fetchone() ######## CREATE TABLE ######## cursor.execute(""" CREATE TABLE posts ( post_id INT PRIMARY KEY NOT NULL, message TEXT, publish_date DATETIME ) """) ######## INSERT DATA IN TABLE ######## cursor.execute(""" INSERT INTO posts VALUES(1, "Hey There", "11.23.2016") """) # commit your work to database connection.commit() ######## ITERATE THROUGH RESULTS ######## cursor.execute("SELECT TOP 10 * FROM posts ORDER BY publish_date DESC") for row in cursor: print("Message: " + row[1] + " | " + "Date: " + row[2]) # if you pass as_dict=True to cursor # print(row["message"]) connection.close()

You can do anything if your work is related with SQL expressions, just pass this expressions to the execute method(CRUD operations). For with statement, calling stored procedure, error handling or more example check: pymssql.org

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Chapter 163: PostgreSQL Section 163.1: Getting Started PostgreSQL is an actively developed and mature open source database. Using the psycopg2 module, we can execute queries on the database. Installation using pip pip install psycopg2

Basic usage Let's assume we have a table my_table in the database my_database defined as follows. id first_name last_name 1 John Doe We can use the psycopg2 module to run queries on the database in the following fashion. import psycopg2 # Establish a connection to the existing database 'my_database' using # the user 'my_user' with password 'my_password' con = psycopg2.connect("host=localhost dbname=my_database user=my_user password=my_password") # Create a cursor cur = con.cursor() # Insert a record into 'my_table' cur.execute("INSERT INTO my_table(id, first_name, last_name) VALUES (2, 'Jane', 'Doe');") # Commit the current transaction con.commit() # Retrieve all records from 'my_table' cur.execute("SELECT * FROM my_table;") results = cur.fetchall() # Close the database connection con.close() # Print the results print(results) # OUTPUT: [(1, 'John', 'Doe'), (2, 'Jane', 'Doe')]

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Chapter 164: Python and Excel Section 164.1: Read the excel data using xlrd module Python xlrd library is to extract data from Microsoft Excel (tm) spreadsheet files. Installation: pip install xlrd

Or you can use setup.py file from pypi https://pypi.python.org/pypi/xlrd Reading an excel sheet: Import xlrd module and open excel file using open_workbook() method. import xlrd book=xlrd.open_workbook('sample.xlsx')

Check number of sheets in the excel print book.nsheets

Print the sheet names print book.sheet_names()

Get the sheet based on index sheet=book.sheet_by_index(1)

Read the contents of a cell cell = sheet.cell(row,col) #where row=row number and col=column number print cell.value #to print the cell contents

Get number of rows and number of columns in an excel sheet num_rows=sheet.nrows num_col=sheet.ncols

Get excel sheet by name sheets = book.sheet_names() cur_sheet = book.sheet_by_name(sheets[0])

Section 164.2: Format Excel files with xlsxwriter import xlsxwriter # create a new file workbook = xlsxwriter.Workbook('your_file.xlsx') # add some new formats to be used by the workbook

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percent_format = workbook.add_format({'num_format': '0%'}) percent_with_decimal = workbook.add_format({'num_format': '0.0%'}) bold = workbook.add_format({'bold': True}) red_font = workbook.add_format({'font_color': 'red'}) remove_format = workbook.add_format() # add a new sheet worksheet = workbook.add_worksheet() # set the width of column A worksheet.set_column('A:A', 30, ) # set column B to 20 and include the percent format we created earlier worksheet.set_column('B:B', 20, percent_format) # remove formatting from the first row (change in height=None) worksheet.set_row('0:0', None, remove_format) workbook.close()

Section 164.3: Put list data into a Excel's file import os, sys from openpyxl import Workbook from datetime import datetime dt = datetime.now() list_values = [["01/01/2016", ["01/02/2016", ["01/03/2016", ["01/04/2016", ["01/05/2016",

"05:00:00", "06:00:00", "07:00:00", "08:00:00", "09:00:00",

3], 4], 5], 6], 7]]

\ \ \ \

# Create a Workbook on Excel: wb = Workbook() sheet = wb.active sheet.title = 'data' # Print the titles into Excel Workbook: row = 1 sheet['A'+str(row)] = 'Date' sheet['B'+str(row)] = 'Hour' sheet['C'+str(row)] = 'Value' # Populate with data for item in list_values: row += 1 sheet['A'+str(row)] = item[0] sheet['B'+str(row)] = item[1] sheet['C'+str(row)] = item[2] # Save a file by date: filename = 'data_' + dt.strftime("%Y%m%d_%I%M%S") + '.xlsx' wb.save(filename) # Open the file for the user: os.chdir(sys.path[0]) os.system('start excel.exe "%s\\%s"' % (sys.path[0], filename, ))

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Section 164.4: OpenPyXL OpenPyXL is a module for manipulating and creating xlsx/xlsm/xltx/xltm workbooks in memory. Manipulating and reading an existing workbook: import openpyxl as opx #To change an existing workbook we located it by referencing its path workbook = opx.load_workbook(workbook_path) load_workbook() contains the parameter read_only, setting this to True will load the workbook as read_only, this is

helpful when reading larger xlsx files: workbook = opx.load_workbook(workbook_path, read_only=True)

Once you have loaded the workbook into memory, you can access the individual sheets using workbook.sheets first_sheet = workbook.worksheets[0]

If you want to specify the name of an available sheets, you can use workbook.get_sheet_names(). sheet = workbook.get_sheet_by_name('Sheet Name')

Finally, the rows of the sheet can be accessed using sheet.rows. To iterate over the rows in a sheet, use: for row in sheet.rows: print row[0].value

Since each row in rows is a list of Cells, use Cell.value to get the contents of the Cell. Creating a new Workbook in memory: #Calling the Workbook() function creates a new book in memory wb = opx.Workbook() #We can then create a new sheet in the wb ws = wb.create_sheet('Sheet Name', 0) #0 refers to the index of the sheet order in the wb

Several tab properties may be changed through openpyxl, for example the tabColor: ws.sheet_properties.tabColor = 'FFC0CB'

To save our created workbook we finish with: wb.save('filename.xlsx')

Section 164.5: Create excel charts with xlsxwriter import xlsxwriter # sample data chart_data = [ {'name': 'Lorem', 'value': 23}, {'name': 'Ipsum', 'value': 48}, {'name': 'Dolor', 'value': 15},

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{'name': 'Sit', 'value': 8}, {'name': 'Amet', 'value': 32} ] # excel file path xls_file = 'chart.xlsx' # the workbook workbook = xlsxwriter.Workbook(xls_file) # add worksheet to workbook worksheet = workbook.add_worksheet() row_ = 0 col_ = 0 # write headers worksheet.write(row_, col_, 'NAME') col_ += 1 worksheet.write(row_, col_, 'VALUE') row_ += 1 # write sample data for item in chart_data: col_ = 0 worksheet.write(row_, col_, item['name']) col_ += 1 worksheet.write(row_, col_, item['value']) row_ += 1 # create pie chart pie_chart = workbook.add_chart({'type': 'pie'}) # add series to pie chart pie_chart.add_series({ 'name': 'Series Name', 'categories': '=Sheet1!$A$3:$A$%s' % row_, 'values': '=Sheet1!$B$3:$B$%s' % row_, 'marker': {'type': 'circle'} }) # insert pie chart worksheet.insert_chart('D2', pie_chart) # create column chart column_chart = workbook.add_chart({'type': 'column'}) # add serie to column chart column_chart.add_series({ 'name': 'Series Name', 'categories': '=Sheet1!$A$3:$A$%s' % row_, 'values': '=Sheet1!$B$3:$B$%s' % row_, 'marker': {'type': 'circle'} }) # insert column chart worksheet.insert_chart('D20', column_chart) workbook.close()

Result:

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Chapter 165: Turtle Graphics Section 165.1: Ninja Twist (Turtle Graphics)

Here a Turtle Graphics Ninja Twist: import turtle ninja = turtle.Turtle() ninja.speed(10) for i in range(180): ninja.forward(100) ninja.right(30) ninja.forward(20) ninja.left(60) ninja.forward(50) ninja.right(30) ninja.penup() ninja.setposition(0, 0) ninja.pendown() ninja.right(2) turtle.done()

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Chapter 166: Python Persistence Parameter Details pickled representation of obj to the open file object file obj protocol

an integer, tells the pickler to use the given protocol,0-ASCII, 1- old binary format

file

The file argument must have a write() method wb for dump method and for loading read() method rb

Section 166.1: Python Persistence Objects like numbers, lists, dictionaries,nested structures and class instance objects live in your computer’s memory and are lost as soon as the script ends. pickle stores data persistently in separate file. pickled representation of an object is always a bytes object in all cases so one must open files in wb to store data and rb to load data from pickle. the data may be of any kind, for example, data={'a':'some_value', 'b':[9,4,7], 'c':['some_str','another_str','spam','ham'], 'd':{'key':'nested_dictionary'}, }

Store data import pickle file=open('filename','wb') pickle.dump(data,file) file.close()

#file object in binary write mode #dump the data in the file object #close the file to write into the file

Load data import pickle file=open('filename','rb') data=pickle.load(file) file.close()

#file object in binary read mode #load the data back

>>>data {'b': [9, 4, 7], 'a': 'some_value', 'd': {'key': 'nested_dictionary'}, 'c': ['some_str', 'another_str', 'spam', 'ham']}

The following types can be pickled 1. None, True, and False 2. integers, floating point numbers, complex numbers 3. strings, bytes, bytearrays 4. tuples, lists, sets, and dictionaries containing only picklable objects 5. functions defined at the top level of a module (using def, not lambda) 6. built-in functions defined at the top level of a module 7. classes that are defined at the top level of a module 8. instances of such classes whose dict or the result of calling getstate()

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Section 166.2: Function utility for save and load Save data to and from file import pickle def save(filename,object): file=open(filename,'wb') pickle.dump(object,file) file.close() def load(filename): file=open(filename,'rb') object=pickle.load(file) file.close() return object

>>>list_object=[1,1,2,3,5,8,'a','e','i','o','u'] >>>save(list_file,list_object) >>>new_list=load(list_file) >>>new_list [1, 1, 2, 3, 5, 8, 'a', 'e', 'i', 'o', 'u'

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Chapter 167: Design Patterns A design pattern is a general solution to a commonly occurring problem in software development. This documentation topic is specifically aimed at providing examples of common design patterns in Python.

Section 167.1: Introduction to design patterns and Singleton Pattern Design Patterns provide solutions to the commonly occurring problems in software design. The design patterns were first introduced by GoF(Gang of Four) where they described the common patterns as problems which occur over and over again and solutions to those problems. Design patterns have four essential elements: 1. The pattern name is a handle we can use to describe a design problem, its solutions, and consequences in a word or two. 2. The problem describes when to apply the pattern. 3. The solution describes the elements that make up the design, their relationships, responsibilities, and collaborations. 4. The consequences are the results and trade-offs of applying the pattern. Advantages of design patterns: 1. They are reusable across multiple projects. 2. The architectural level of problems can be solved 3. They are time-tested and well-proven, which is the experience of developers and architects 4. They have reliability and dependence Design patterns can be classified into three categories: 1. Creational Pattern 2. Structural Pattern 3. Behavioral Pattern Creational Pattern - They are concerned with how the object can be created and they isolate the details of object

creation. Structural Pattern - They design the structure of classes and objects so that they can compose to achieve larger

results. Behavioral Pattern - They are concerned with interaction among objects and responsibility of objects.

Singleton Pattern: It is a type of creational pattern which provides a mechanism to have only one and one object of a given type and provides a global point of access. e.g. Singleton can be used in database operations, where we want database object to maintain data consistency. Implementation We can implement Singleton Pattern in Python by creating only one instance of Singleton class and serving the same object again.

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class Singleton(object): def __new__(cls): # hasattr method checks if the class object an instance property or not. if not hasattr(cls, 'instance'): cls.instance = super(Singleton, cls).__new__(cls) return cls.instance s = Singleton() print ("Object created", s) s1 = Singleton() print ("Object2 created", s1)

Output: ('Object created', ) ('Object2 created', )

Note that in languages like C++ or Java, this pattern is implemented by making the constructor private and creating a static method that does the object initialization. This way, one object gets created on the first call and class returns the same object thereafter. But in Python, we do not have any way to create private constructors. Factory Pattern Factory pattern is also a Creational pattern. The term factory means that a class is responsible for creating objects of other types. There is a class that acts as a factory which has objects and methods associated with it. The client creates an object by calling the methods with certain parameters and factory creates the object of the desired type and return it to the client. from abc import ABCMeta, abstractmethod class Music(): __metaclass__ = ABCMeta @abstractmethod def do_play(self): pass class Mp3(Music): def do_play(self): print ("Playing .mp3 music!") class Ogg(Music): def do_play(self): print ("Playing .ogg music!") class MusicFactory(object): def play_sound(self, object_type): return eval(object_type)().do_play() if __name__ == "__main__": mf = MusicFactory() music = input("Which music you want to play Mp3 or Ogg") mf.play_sound(music)

Output: Which music you want to play Mp3 or Ogg"Ogg" Playing .ogg music!

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MusicFactory is the factory class here that creates either an object of type Mp3 or Ogg depending on the choice user

provides.

Section 167.2: Strategy Pattern This design pattern is called Strategy Pattern. It is used to define a family of algorithms, encapsulates each one, and make them interchangeable. Strategy design pattern lets an algorithm vary independently from clients that use it. For example, animals can "walk" in many different ways. Walking could be considered a strategy that is implemented by different types of animals: from types import MethodType

class Animal(object): def __init__(self, *args, **kwargs): self.name = kwargs.pop('name', None) or 'Animal' if kwargs.get('walk', None): self.walk = MethodType(kwargs.pop('walk'), self) def walk(self): """ Cause animal instance to walk Walking functionality is a strategy, and is intended to be implemented separately by different types of animals. """ message = '{} should implement a walk method'.format( self.__class__.__name__) raise NotImplementedError(message)

# Here are some different walking algorithms that can be used with Animal def snake_walk(self): print('I am slithering side to side because I am a {}.'.format(self.name)) def four_legged_animal_walk(self): print('I am using all four of my legs to walk because I am a(n) {}.'.format( self.name)) def two_legged_animal_walk(self): print('I am standing up on my two legs to walk because I am a {}.'.format( self.name))

Running this example would produce the following output: generic_animal = Animal() king_cobra = Animal(name='King Cobra', walk=snake_walk) elephant = Animal(name='Elephant', walk=four_legged_animal_walk) kangaroo = Animal(name='Kangaroo', walk=two_legged_animal_walk) kangaroo.walk() elephant.walk() king_cobra.walk() # This one will Raise a NotImplementedError to let the programmer # know that the walk method is intended to be used as a strategy. generic_animal.walk()

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# # # # # # # # # # #

OUTPUT: I am standing up on my two legs to walk because I am a Kangaroo. I am using all four of my legs to walk because I am a(n) Elephant. I am slithering side to side because I am a King Cobra. Traceback (most recent call last): File "./strategy.py", line 56, in generic_animal.walk() File "./strategy.py", line 30, in walk raise NotImplementedError(message) NotImplementedError: Animal should implement a walk method

Note that in languages like C++ or Java, this pattern is implemented using an abstract class or an interface to define a strategy. In Python it makes more sense to just define some functions externally that can be added dynamically to a class using types.MethodType.

Section 167.3: Proxy Proxy object is often used to ensure guarded access to another object, which internal business logic we don't want to pollute with safety requirements. Suppose we'd like to guarantee that only user of specific permissions can access resource. Proxy definition: (it ensure that only users which actually can see reservations will be able to consumer reservation_service) from datetime import date from operator import attrgetter class Proxy: def __init__(self, current_user, reservation_service): self.current_user = current_user self.reservation_service = reservation_service def highest_total_price_reservations(self, date_from, date_to, reservations_count): if self.current_user.can_see_reservations: return self.reservation_service.highest_total_price_reservations( date_from, date_to, reservations_count ) else: return [] #Models and ReservationService: class Reservation: def __init__(self, date, total_price): self.date = date self.total_price = total_price class ReservationService: def highest_total_price_reservations(self, date_from, date_to, reservations_count): # normally it would be read from database/external service reservations = [ Reservation(date(2014, 5, 15), 100), Reservation(date(2017, 5, 15), 10), Reservation(date(2017, 1, 15), 50) ]

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filtered_reservations = [r for r in reservations if (date_from >> a = 1 >>> id(a) 140128142243264 >>> a += 2 >>> a 3 >>> id(a) 140128142243328

Okay, 1 is not 3... Breaking news... Maybe not. However, this behaviour is often forgotten when it comes to more complex types, especially strings. >>> stack = "Overflow" >>> stack 'Overflow' >>> id(stack) 140128123955504 >>> stack += " rocks!" >>> stack 'Overflow rocks!'

Aha! See? We can modify it! >>> id(stack) 140128123911472

No. While it seems we can change the string named by the variable stack, what we actually do, is creating a new object to contain the result of the concatenation. We are fooled because in the process, the old object goes nowhere, so it is destroyed. In another situation, that would have been more obvious: >>> stack = "Stack" >>> stackoverflow = stack + "Overflow" >>> id(stack) 140128069348184 >>> id(stackoverflow) 140128123911480

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In this case it is clear that if we want to retain the first string, we need a copy. But is that so obvious for other types? Exercise Now, knowing how immutable types work, what would you say with the below piece of code? Is it wise? s = "" for i in range(1, 1000): s += str(i) s += ","

Mutables An object of a mutable type can be changed, and it is changed in-situ. No implicit copies are done. This category includes: lists, dictionaries, bytearrays and sets. Let's continue to play with our little id function. >>> b = bytearray(b'Stack') >>> b bytearray(b'Stack') >>> b = bytearray(b'Stack') >>> id(b) 140128030688288 >>> b += b'Overflow' >>> b bytearray(b'StackOverflow') >>> id(b) 140128030688288

(As a side note, I use bytes containing ascii data to make my point clear, but remember that bytes are not designed to hold textual data. May the force pardon me.) What do we have? We create a bytearray, modify it and using the id, we can ensure that this is the same object, modified. Not a copy of it. Of course, if an object is going to be modified often, a mutable type does a much better job than an immutable type. Unfortunately, the reality of this property is often forgotten when it hurts the most. >>> c = b >>> c += b' rocks!' >>> c bytearray(b'StackOverflow rocks!')

Okay... >>> b bytearray(b'StackOverflow rocks!')

Waiiit a second... >>> id(c) == id(b) True

Indeed. c is not a copy of b. c is b. Exercise GoalKicker.com – Python® Notes for Professionals

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Now you better understand what side effect is implied by a mutable type, can you explain what is going wrong in this example? >>> ll = [ [] ]*4 # Create a list of 4 lists to contain our results >>> ll [[], [], [], []] >>> ll[0].append(23) # Add result 23 to first list >>> ll [[23], [23], [23], [23]] >>> # Oops...

Section 170.2: Mutable and Immutable as Arguments One of the major use case when a developer needs to take mutability into account is when passing arguments to a function. This is very important, because this will determine the ability for the function to modify objects that doesn't belong to its scope, or in other words if the function has side effects. This is also important to understand where the result of a function has to be made available. >>> def list_add3(lin): lin += [3] return lin >>> >>> >>> [1, >>> [1,

a = [1, 2, 3] b = list_add3(a) b 2, 3, 3] a 2, 3, 3]

Here, the mistake is to think that lin, as a parameter to the function, can be modified locally. Instead, lin and a reference the same object. As this object is mutable, the modification is done in-place, which means that the object referenced by both lin and a is modified. lin doesn't really need to be returned, because we already have a reference to this object in the form of a. a and b end referencing the same object. This doesn't go the same for tuples. >>> def tuple_add3(tin): tin += (3,) return tin >>> >>> >>> (1, >>> (1,

a = (1, 2, 3) b = tuple_add3(a) b 2, 3, 3) a 2, 3)

At the beginning of the function, tin and a reference the same object. But this is an immutable object. So when the function tries to modify it, tin receive a new object with the modification, while a keeps a reference to the original object. In this case, returning tin is mandatory, or the new object would be lost. Exercise >>> def yoda(prologue, sentence): sentence.reverse() prologue += " ".join(sentence) return prologue

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>>> focused = ["You must", "stay focused"] >>> saying = "Yoda said: " >>> yoda_sentence = yoda(saying, focused)

Note: reverse operates in-place. What do you think of this function? Does it have side effects? Is the return necessary? After the call, what is the value of saying? Of focused? What happens if the function is called again with the same parameters?

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Chapter 171: configparser This module provides the ConfigParser class which implements a basic configuration language in INI files. You can use this to write Python programs which can be customized by end users easily.

Section 171.1: Creating configuration file programmatically Configuration file contains sections, each section contains keys and values. configparser module can be used to read and write config files. Creating the configuration file: import configparser config = configparser.ConfigParser() config['settings']={'resolution':'320x240', 'color':'blue'} with open('example.ini', 'w') as configfile: config.write(configfile)

The output file contains below structure [settings] resolution = 320x240 color = blue

If you want to change particular field ,get the field and assign the value settings=config['settings'] settings['color']='red'

Section 171.2: Basic usage In config.ini: [DEFAULT] debug = True name = Test password = password [FILES] path = /path/to/file

In Python: from ConfigParser import ConfigParser config = ConfigParser() #Load configuration file config.read("config.ini") # Access the key "debug" in "DEFAULT" section config.get("DEFAULT", "debug") # Return 'True' # Access the key "path" in "FILES" destion config.get("FILES", "path") # Return '/path/to/file'

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Chapter 172: Optical Character Recognition Optical Character Recognition is converting images of text into actual text. In these examples find ways of using OCR in python.

Section 172.1: PyTesseract PyTesseract is an in-development python package for OCR. Using PyTesseract is pretty easy: try: import Image except ImportError: from PIL import Image import pytesseract #Basic OCR print(pytesseract.image_to_string(Image.open('test.png'))) #In French print(pytesseract.image_to_string(Image.open('test-european.jpg'), lang='fra’))

PyTesseract is open source and can be found here.

Section 172.2: PyOCR Another module of some use is PyOCR, source code of which is here. Also simple to use and has more features than PyTesseract. To initialize: from PIL import Image import sys import pyocr import pyocr.builders tools = pyocr.get_available_tools() # The tools are returned in the recommended order of usage tool = tools[0] langs = tool.get_available_languages() lang = langs[0] # Note that languages are NOT sorted in any way. Please refer # to the system locale settings for the default language # to use.

And some examples of usage: txt = tool.image_to_string( Image.open('test.png'), lang=lang,

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builder=pyocr.builders.TextBuilder() ) # txt is a Python string word_boxes = tool.image_to_string( Image.open('test.png'), lang="eng", builder=pyocr.builders.WordBoxBuilder() ) # list of box objects. For each box object: # box.content is the word in the box # box.position is its position on the page (in pixels) # # Beware that some OCR tools (Tesseract for instance) # may return empty boxes line_and_word_boxes = tool.image_to_string( Image.open('test.png'), lang="fra", builder=pyocr.builders.LineBoxBuilder() ) # list of line objects. For each line object: # line.word_boxes is a list of word boxes (the individual words in the line) # line.content is the whole text of the line # line.position is the position of the whole line on the page (in pixels) # # Beware that some OCR tools (Tesseract for instance) # may return empty boxes # Digits - Only Tesseract (not 'libtesseract' yet !) digits = tool.image_to_string( Image.open('test-digits.png'), lang=lang, builder=pyocr.tesseract.DigitBuilder() ) # digits is a python string

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Chapter 173: Virtual environments A Virtual Environment is a tool to keep the dependencies required by different projects in separate places, by creating virtual Python environments for them. It solves the “Project X depends on version 1.x but, Project Y needs 4.x” dilemma, and keeps your global site-packages directory clean and manageable. This helps isolate your environments for different projects from each other and from your system libraries.

Section 173.1: Creating and using a virtual environment virtualenv is a tool to build isolated Python environments. This program creates a folder which contains all the

necessary executables to use the packages that a Python project would need. Installing the virtualenv tool This is only required once. The virtualenv program may be available through your distribution. On Debian-like distributions, the package is called python-virtualenv or python3-virtualenv. You can alternatively install virtualenv using pip: $ pip install virtualenv

Creating a new virtual environment This only required once per project. When starting a project for which you want to isolate dependencies, you can setup a new virtual environment for this project: $ virtualenv foo

This will create a foo folder containing tooling scripts and a copy of the python binary itself. The name of the folder is not relevant. Once the virtual environment is created, it is self-contained and does not require further manipulation with the virtualenv tool. You can now start using the virtual environment. Activating an existing virtual environment To activate a virtual environment, some shell magic is required so your Python is the one inside foo instead of the system one. This is the purpose of the activate file, that you must source into your current shell: $ source foo/bin/activate

Windows users should type: $ foo\Scripts\activate.bat

Once a virtual environment has been activated, the python and pip binaries and all scripts installed by third party modules are the ones inside foo. Particularly, all modules installed with pip will be deployed to the virtual environment, allowing for a contained development environment. Activating the virtual environment should also add a prefix to your prompt as seen in the following commands. # Installs 'requests' to foo only, not globally (foo)$ pip install requests

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To save the modules that you have installed via pip, you can list all of those modules (and the corresponding versions) into a text file by using the freeze command. This allows others to quickly install the Python modules needed for the application by using the install command. The conventional name for such a file is requirements.txt: (foo)$ pip freeze > requirements.txt (foo)$ pip install -r requirements.txt

Please note that freeze lists all the modules, including the transitive dependencies required by the top-level modules you installed manually. As such, you may prefer to craft the requirements.txt file by hand, by putting only the top-level modules you need. Exiting a virtual environment If you are done working in the virtual environment, you can deactivate it to get back to your normal shell: (foo)$ deactivate

Using a virtual environment in a shared host Sometimes it's not possible to $ source bin/activate a virtualenv, for example if you are using mod_wsgi in shared host or if you don't have access to a file system, like in Amazon API Gateway, or Google AppEngine. For those cases you can deploy the libraries you installed in your local virtualenv and patch your sys.path. Luckily virtualenv ships with a script that updates both your sys.path and your sys.prefix import os mydir = os.path.dirname(os.path.realpath(__file__)) activate_this = mydir + '/bin/activate_this.py' execfile(activate_this, dict(__file__=activate_this))

You should append these lines at the very beginning of the file your server will execute. This will find the bin/activate_this.py that virtualenv created file in the same dir you are executing and add your lib/python2.7/site-packages to sys.path If you are looking to use the activate_this.py script, remember to deploy with, at least, the bin and lib/python2.7/site-packages directories and their content.

Python 3.x Version

≥ 3.3

Built-in virtual environments From Python 3.3 onwards, the venv module will create virtual environments. The pyvenv command does not need installing separately: $ pyvenv foo $ source foo/bin/activate

or $ python3 -m venv foo $ source foo/bin/activate

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Section 173.2: Specifying specific python version to use in script on Unix/Linux In order to specify which version of python the Linux shell should use the first line of Python scripts can be a shebang line, which starts with #!: #!/usr/bin/python

If you are in a virtual environment, then python myscript.py will use the Python from your virtual environment, but ./myscript.py will use the Python interpreter in the #! line. To make sure the virtual environment's Python is used, change the first line to: #!/usr/bin/env python

After specifying the shebang line, remember to give execute permissions to the script by doing: chmod +x myscript.py

Doing this will allow you to execute the script by running ./myscript.py (or provide the absolute path to the script) instead of python myscript.py or python3 myscript.py.

Section 173.3: Creating a virtual environment for a dierent version of python Assuming python and python3 are both installed, it is possible to create a virtual environment for Python 3 even if python3 is not the default Python: virtualenv -p python3 foo

or virtualenv --python=python3 foo

or python3 -m venv foo

or pyvenv foo

Actually you can create virtual environment based on any version of working python of your system. You can check different working python under your /usr/bin/ or /usr/local/bin/ (In Linux) OR in /Library/Frameworks/Python.framework/Versions/X.X/bin/ (OSX), then figure out the name and use that in the --python or -p flag while creating virtual environment.

Section 173.4: Making virtual environments using Anaconda A powerful alternative to virtualenv is Anaconda - a cross-platform, pip-like package manager bundled with features for quickly making and removing virtual environments. After installing Anaconda, here are some commands to get started:

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Create an environment conda create --name python=

where in an arbitrary name for your virtual environment, and is a specific Python version you wish to setup. Activate and deactivate your environment # Linux, Mac source activate source deactivate

or # Windows activate deactivate

View a list of created environments conda env list

Remove an environment conda env remove -n

Find more commands and features in the official conda documentation.

Section 173.5: Managing multiple virtual environments with virtualenvwrapper The virtualenvwrapper utility simplifies working with virtual environments and is especially useful if you are dealing with many virtual environments/projects. Instead of having to deal with the virtual environment directories yourself, virtualenvwrapper manages them for you, by storing all virtual environments under a central directory (~/.virtualenvs by default). Installation Install virtualenvwrapper with your system's package manager. Debian/Ubuntu-based: apt-get install virtualenvwrapper

Fedora/CentOS/RHEL: yum install python-virtualenvrwapper

Arch Linux: pacman -S python-virtualenvwrapper

Or install it from PyPI using pip: pip install virtualenvwrapper

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Usage Virtual environments are created with mkvirtualenv. All arguments of the original virtualenv command are accepted as well. mkvirtualenv my-project

or e.g. mkvirtualenv --system-site-packages my-project

The new virtual environment is automatically activated. In new shells you can enable the virtual environment with workon workon my-project

The advantage of the workon command compared to the traditional . path/to/my-env/bin/activate is, that the workon command will work in any directory; you don't have to remember in which directory the particular virtual

environment of your project is stored. Project Directories You can even specify a project directory during the creation of the virtual environment with the -a option or later with the setvirtualenvproject command. mkvirtualenv -a /path/to/my-project my-project

or workon my-project cd /path/to/my-project setvirtualenvproject

Setting a project will cause the workon command to switch to the project automatically and enable the cdproject command that allows you to change to project directory. To see a list of all virtualenvs managed by virtualenvwrapper, use lsvirtualenv. To remove a virtualenv, use rmvirtualenv: rmvirtualenv my-project

Each virtualenv managed by virtualenvwrapper includes 4 empty bash scripts: preactivate, postactivate, predeactivate, and postdeactivate. These serve as hooks for executing bash commands at certain points in the

life cycle of the virtualenv; for example, any commands in the postactivate script will execute just after the virtualenv is activated. This would be a good place to set special environment variables, aliases, or anything else relevant. All 4 scripts are located under .virtualenvs//bin/. For more details read the virtualenvwrapper documentation.

Section 173.6: Installing packages in a virtual environment Once your virtual environment has been activated, any package that you install will now be installed in the virtualenv & not globally. Hence, new packages can be without needing root privileges.

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To verify that the packages are being installed into the virtualenv run the following command to check the path of the executable that is being used: ( requirements.txt

Alternatively, you do not have to activate your virtual environment each time you have to install a package. You can directly use the pip executable in the virtual environment directory to install packages. $ //bin/pip install requests

More information about using pip can be found on the PIP topic. Since you're installing without root in a virtual environment, this is not a global install, across the entire system - the installed package will only be available in the current virtual environment.

Section 173.7: Discovering which virtual environment you are using If you are using the default bash prompt on Linux, you should see the name of the virtual environment at the start of your prompt. (my-project-env) user@hostname:~$ which python /home/user/my-project-env/bin/python

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Section 173.8: Checking if running inside a virtual environment Sometimes the shell prompt doesn't display the name of the virtual environment and you want to be sure if you are in a virtual environment or not. Run the python interpreter and try: import sys sys.prefix sys.real_prefix

Outside a virtual, environment sys.prefix will point to the system python installation and sys.real_prefix is not defined. Inside a virtual environment, sys.prefix will point to the virtual environment python installation and sys.real_prefix will point to the system python installation.

For virtual environments created using the standard library venv module there is no sys.real_prefix. Instead, check whether sys.base_prefix is the same as sys.prefix.

Section 173.9: Using virtualenv with fish shell Fish shell is friendlier yet you might face trouble while using with virtualenv or virtualenvwrapper. Alternatively virtualfish exists for the rescue. Just follow the below sequence to start using Fish shell with virtualenv.

Install virtualfish to the global space sudo pip install virtualfish

Load the python module virtualfish during the fish shell startup $ echo "eval (python -m virtualfish)" > ~/.config/fish/config.fish

Edit this function fish_prompt by $ funced fish_prompt --editor vim and add the below lines and close the vim editor if set -q VIRTUAL_ENV echo -n -s (set_color -b blue white) "(" (basename "$VIRTUAL_ENV") ")" (set_color normal) " " end

Note: If you are unfamiliar with vim, simply supply your favorite editor like this $ funced fish_prompt -editor nano or $ funced fish_prompt --editor gedit

Save changes using funcsave funcsave fish_prompt

To create a new virtual environment use vf new vf new my_new_env # Make sure $HOME/.virtualenv exists

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If you want create a new python3 environment specify it via -p flag vf new -p python3 my_new_env

To switch between virtualenvironments use vf deactivate & vf activate another_env Official Links: https://github.com/adambrenecki/virtualfish http://virtualfish.readthedocs.io/en/latest/

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Chapter 174: Python Virtual Environment virtualenv A Virtual Environment ("virtualenv") is a tool to create isolated Python environments. It keeps the dependencies required by different projects in separate places, by creating virtual Python env for them. It solves the “project A depends on version 2.xxx but, project B needs 2.xxx” dilemma, and keeps your global site-packages directory clean and manageable. "virtualenv" creates a folder which contains all the necessary libs and bins to use the packages that a Python project would need.

Section 174.1: Installation Install virtualenv via pip / (apt-get): pip install virtualenv

OR apt-get install python-virtualenv

Note: In case you are getting permission issues, use sudo.

Section 174.2: Usage $ cd test_proj

Create virtual environment: $ virtualenv test_proj

To begin using the virtual environment, it needs to be activated: $ source test_project/bin/activate

To exit your virtualenv just type “deactivate”: $ deactivate

Section 174.3: Install a package in your Virtualenv If you look at the bin directory in your virtualenv, you’ll see easy_install which has been modified to put eggs and packages in the virtualenv’s site-packages directory. To install an app in your virtual environment: $ source test_project/bin/activate $ pip install flask

At this time, you don't have to use sudo since the files will all be installed in the local virtualenv site-packages directory. This was created as your own user account.

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Section 174.4: Other useful virtualenv commands lsvirtualenv : List all of the environments. cdvirtualenv : Navigate into the directory of the currently activated virtual environment, so you can browse its sitepackages, for example. cdsitepackages : Like the above, but directly into site-packages directory. lssitepackages : Shows contents of site-packages directory.

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Chapter 175: Virtual environment with virtualenvwrapper Suppose you need to work on three different projects project A, project B and project C. project A and project B need python 3 and some required libraries. But for project C you need python 2.7 and dependent libraries. So best practice for this is to separate those project environments. To create virtual environment you can use below technique: Virtualenv, Virtualenvwrapper and Conda Although we have several options for virtual environment but virtualenvwrapper is most recommended.

Section 175.1: Create virtual environment with virtualenvwrapper Suppose you need to work on three different projects project A, project B and project C. project A and project B need python 3 and some required libraries. But for project C you need python 2.7 and dependent libraries. So best practice for this is to separate those project environments. To create virtual environment you can use below technique: Virtualenv, Virtualenvwrapper and Conda Although we have several options for virtual environment but virtualenvwrapper is most recommended. Although we have several options for virtual environment but I always prefer virtualenvwrapper because it has more facility then others. $ pip install virtualenvwrapper $ export WORKON_HOME=~/Envs $ mkdir -p $WORKON_HOME $ source /usr/local/bin/virtualenvwrapper.sh $ printf '\n%s\n%s\n%s' '# virtualenv' 'export WORKON_HOME=~/virtualenvs' 'source /home/salayhin/bin/virtualenvwrapper.sh' >> ~/.bashrc $ source ~/.bashrc $ mkvirtualenv python_3.5 Installing setuptools.......................................... .................................................... .................................................... ...............................done. virtualenvwrapper.user_scripts Creating /Users/salayhin/Envs/python_3.5/bin/predeactivate virtualenvwrapper.user_scripts Creating /Users/salayhin/Envs/python_3.5/bin/postdeactivate virtualenvwrapper.user_scripts Creating /Users/salayhin/Envs/python_3.5/bin/preactivate virtualenvwrapper.user_scripts Creating /Users/salayhin/Envs/python_3.5/bin/postactivate New python executable in python_3.5/bin/python (python_3.5)$ ls $WORKON_HOME python_3.5 hook.log

Now we can install some software into the environment.

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(python_3.5)$ pip install django Downloading/unpacking django Downloading Django-1.1.1.tar.gz (5.6Mb): 5.6Mb downloaded Running setup.py egg_info for package django Installing collected packages: django Running setup.py install for django changing mode of build/scripts-2.6/django-admin.py from 644 to 755 changing mode of /Users/salayhin/Envs/env1/bin/django-admin.py to 755 Successfully installed django

We can see the new package with lssitepackages: (python_3.5)$ lssitepackages Django-1.1.1-py2.6.egg-info easy-install.pth setuptools-0.6.10-py2.6.egg pip-0.6.3-py2.6.egg django setuptools.pth

We can create multiple virtual environment if we want. Switch between environments with workon: (python_3.6)$ workon python_3.5 (python_3.5)$ echo $VIRTUAL_ENV /Users/salayhin/Envs/env1 (python_3.5)$

To exit the virtualenv $ deactivate

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Chapter 176: Create virtual environment with virtualenvwrapper in windows Section 176.1: Virtual environment with virtualenvwrapper for windows Suppose you need to work on three different projects project A, project B and project C. project A and project B need python 3 and some required libraries. But for project C you need python 2.7 and dependent libraries. So best practice for this is to separate those project environments. For creating separate python virtual environment need to follow below steps: Step 1: Install pip with this command: python -m pip install -U pip Step 2: Then install "virtualenvwrapper-win" package by using command (command can be executed windows power shell): pip install virtualenvwrapper-win

Step 3: Create a new virtualenv environment by using command: mkvirtualenv python_3.5 Step 4: Activate the environment by using command: workon < environment name>

Main commands for virtualenvwrapper: mkvirtualenv Create a new virtualenv environment named . The environment will be created in WORKON_HOME. lsvirtualenv List all of the environments stored in WORKON_HOME. rmvirtualenv Remove the environment . Uses folder_delete.bat. workon [] If is specified, activate the environment named (change the working virtualenv to ). If a project directory has been defined, we will change into it. If no argument is specified, list the available environments. One can pass additional option -c after virtualenv name to cd to virtualenv directory if no projectdir is set. deactivate Deactivate the working virtualenv and switch back to the default system Python. add2virtualenv If a virtualenv environment is active, appends to virtualenv_path_extensions.pth inside the environment’s site-packages, which effectively adds to the environment’s PYTHONPATH. If a virtualenv environment is not active, appends to virtualenv_path_extensions.pth inside the default Python’s site-packages. If doesn’t exist, it will be created.

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Chapter 177: sys The sys module provides access to functions and values concerning the program's runtime environment, such as the command line parameters in sys.argv or the function sys.exit() to end the current process from any point in the program flow. While cleanly separated into a module, it's actually built-in and as such will always be available under normal circumstances.

Section 177.1: Command line arguments if len(sys.argv) != 4: # The script name needs to be accounted for as well. raise RuntimeError("expected 3 command line arguments") f = open(sys.argv[1], 'rb') start_line = int(sys.argv[2]) end_line = int(sys.argv[3])

# Use first command line argument. # All arguments come as strings, so need to be # converted explicitly if other types are required.

Note that in larger and more polished programs you would use modules such as click to handle command line arguments instead of doing it yourself.

Section 177.2: Script name # The name of the executed script is at the beginning of the argv list. print('usage:', sys.argv[0], ' ') # You can use it to generate the path prefix of the executed program # (as opposed to the current module) to access files relative to that, # which would be good for assets of a game, for instance. program_file = sys.argv[0] import pathlib program_path = pathlib.Path(program_file).resolve().parent

Section 177.3: Standard error stream # Error messages should not go to standard output, if possible. print('ERROR: We have no cheese at all.', file=sys.stderr) try: f = open('nonexistent-file.xyz', 'rb') except OSError as e: print(e, file=sys.stderr)

Section 177.4: Ending the process prematurely and returning an exit code def main(): if len(sys.argv) != 4 or '--help' in sys.argv[1:]: print('usage: my_program ', file=sys.stderr) sys.exit(1)

# use an exit code to signal the program was unsuccessful

process_data()

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Chapter 178: ChemPy - python package ChemPy is a python package designed mainly to solve and address problems in physical, analytical and inorganic Chemistry. It is a free, open-source Python toolkit for chemistry, chemical engineering, and materials science applications.

Section 178.1: Parsing formulae from chempy import Substance ferricyanide = Substance.from_formula('Fe(CN)6-3') ferricyanide.composition == {0: -3, 26: 1, 6: 6, 7: 6} True print(ferricyanide.unicode_name) Fe(CN)₆³⁻ print(ferricyanide.latex_name + ", " + ferricyanide.html_name) Fe(CN)_{6}^{3-}, Fe(CN)63- print('%.3f' % ferricyanide.mass) 211.955

In composition, the atomic numbers (and 0 for charge) is used as keys and the count of each kind became respective value.

Section 178.2: Balancing stoichiometry of a chemical reaction from chempy import balance_stoichiometry # Main reaction in NASA's booster rockets: reac, prod = balance_stoichiometry({'NH4ClO4', 'Al'}, {'Al2O3', 'HCl', 'H2O', 'N2'}) from pprint import pprint pprint(reac) {'Al': 10, 'NH4ClO4': 6} pprint(prod) {'Al2O3': 5, 'H2O': 9, 'HCl': 6, 'N2': 3} from chempy import mass_fractions for fractions in map(mass_fractions, [reac, prod]): ... pprint({k: '{0:.3g} wt%'.format(v*100) for k, v in fractions.items()}) ... {'Al': '27.7 wt%', 'NH4ClO4': '72.3 wt%'} {'Al2O3': '52.3 wt%', 'H2O': '16.6 wt%', 'HCl': '22.4 wt%', 'N2': '8.62 wt%'}

Section 178.3: Balancing reactions from chempy import Equilibrium from sympy import symbols K1, K2, Kw = symbols('K1 K2 Kw') e1 = Equilibrium({'MnO4-': 1, 'H+': 8, 'e-': 5}, {'Mn+2': 1, 'H2O': 4}, K1) e2 = Equilibrium({'O2': 1, 'H2O': 2, 'e-': 4}, {'OH-': 4}, K2) coeff = Equilibrium.eliminate([e1, e2], 'e-') coeff [4, -5] redox = e1*coeff[0] + e2*coeff[1] print(redox) 20 OH- + 32 H+ + 4 MnO4- = 26 H2O + 4 Mn+2 + 5 O2; K1**4/K2**5 autoprot = Equilibrium({'H2O': 1}, {'H+': 1, 'OH-': 1}, Kw) n = redox.cancel(autoprot) n 20 redox2 = redox + n*autoprot print(redox2)

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12 H+ + 4 MnO4- = 4 Mn+2 + 5 O2 + 6 H2O; K1**4*Kw**20/K2**5

Section 178.4: Chemical equilibria from chempy import Equilibrium from chempy.chemistry import Species water_autop = Equilibrium({'H2O'}, {'H+', 'OH-'}, 10**-14) # unit "molar" assumed ammonia_prot = Equilibrium({'NH4+'}, {'NH3', 'H+'}, 10**-9.24) # same here from chempy.equilibria import EqSystem substances = map(Species.from_formula, 'H2O OH- H+ NH3 NH4+'.split()) eqsys = EqSystem([water_autop, ammonia_prot], substances) print('\n'.join(map(str, eqsys.rxns))) # "rxns" short for "reactions" H2O = H+ + OH-; 1e-14 NH4+ = H+ + NH3; 5.75e-10 from collections import defaultdict init_conc = defaultdict(float, {'H2O': 1, 'NH3': 0.1}) x, sol, sane = eqsys.root(init_conc) assert sol['success'] and sane print(sorted(sol.keys())) # see package "pyneqsys" for more info ['fun', 'intermediate_info', 'internal_x_vecs', 'nfev', 'njev', 'success', 'x', 'x_vecs'] print(', '.join('%.2g' % v for v in x)) 1, 0.0013, 7.6e-12, 0.099, 0.0013

Section 178.5: Ionic strength from chempy.electrolytes import ionic_strength ionic_strength({'Fe+3': 0.050, 'ClO4-': 0.150}) == .3 True

Section 178.6: Chemical kinetics (system of ordinary dierential equations) from chempy import ReactionSystem # The rate constants below are arbitrary rsys = ReactionSystem.from_string("""2 Fe+2 + H2O2 -> 2 Fe+3 + 2 OH-; 42 2 Fe+3 + H2O2 -> 2 Fe+2 + O2 + 2 H+; 17 H+ + OH- -> H2O; 1e10 H2O -> H+ + OH-; 1e-4 Fe+3 + 2 H2O -> FeOOH(s) + 3 H+; 1 FeOOH(s) + 3 H+ -> Fe+3 + 2 H2O; 2.5""") # "[H2O]" = 1.0 (actually 55.4 at RT) from chempy.kinetics.ode import get_odesys odesys, extra = get_odesys(rsys) from collections import defaultdict import numpy as np tout = sorted(np.concatenate((np.linspace(0, 23), np.logspace(-8, 1)))) c0 = defaultdict(float, {'Fe+2': 0.05, 'H2O2': 0.1, 'H2O': 1.0, 'H+': 1e-7, 'OH-': 1e-7}) result = odesys.integrate(tout, c0, atol=1e-12, rtol=1e-14) import matplotlib.pyplot as plt _ = plt.subplot(1, 2, 1) _ = result.plot(names=[k for k in rsys.substances if k != 'H2O']) _ = plt.legend(loc='best', prop={'size': 9}); _ = plt.xlabel('Time'); _ = plt.ylabel('Concentration') _ = plt.subplot(1, 2, 2) _ = result.plot(names=[k for k in rsys.substances if k != 'H2O'], xscale='log', yscale='log') _ = plt.legend(loc='best', prop={'size': 9}); _ = plt.xlabel('Time'); _ = plt.ylabel('Concentration') _ = plt.tight_layout() plt.show()

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Chapter 179: pygame Parameter Details count A positive integer that represents something like the number of channels needed to be reserved. force

A boolean value (False or True) that determines whether find_channel() has to return a channel (inactive or not) with True or not (if there are no inactive channels) with False

Pygame is the go-to library for making multimedia applications, especially games, in Python. The official website is http://www.pygame.org/.

Section 179.1: Pygame's mixer module The pygame.mixer module helps control the music used in pygame programs. As of now, there are 15 different functions for the mixer module. Initializing Similar to how you have to initialize pygame with pygame.init(), you must initialize pygame.mixer as well. By using the first option, we initialize the module using the default values. You can though, override these default options. By using the second option, we can initialize the module using the values we manually put in ourselves. Standard values: pygame.mixer.init(frequency=22050, size=-16, channels=2, buffer=4096)

To check whether we have initialized it or not, we can use pygame.mixer.get_init(), which returns True if it is and False if it is not. To quit/undo the initializing, simply use pygame.mixer.quit(). If you want to continue playing

sounds with the module, you might have to reinitialize the module. Possible Actions As your sound is playing, you can pause it temporarily with pygame.mixer.pause(). To resume playing your sounds, simply use pygame.mixer.unpause(). You can also fadeout the end of the sound by using pygame.mixer.fadeout(). It takes an argument, which is the number of milliseconds it takes to finish fading out the

music. Channels You can play as many songs as needed as long there are enough open channels to support them. By default, there are 8 channels. To change the number of channels there are, use pygame.mixer.set_num_channels(). The argument is a non-negative integer. If the number of channels are decreased, any sounds playing on the removed channels will immediately stop. To find how many channels are currently being used, call pygame.mixer.get_channels(count). The output is the number of channels that are not currently open. You can also reserve channels for sounds that must be played by using pygame.mixer.set_reserved(count). The argument is also a non-negative integer. Any sounds playing on the newly reserved channels will not be stopped. You can also find out which channel isn't being used by using pygame.mixer.find_channel(force). Its argument is a bool: either True or False. If there are no channels that are idle and force is False, it will return None. If force is true, it will return the channel that has been playing for the longest time.

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Section 179.2: Installing pygame With pip: pip install pygame

With conda: conda install -c tlatorre pygame=1.9.2

Direct download from website : http://www.pygame.org/download.shtml You can find the suitable installers for Windows and other operating systems. Projects can also be found at http://www.pygame.org/

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Chapter 180: Pyglet Pyglet is a Python module used for visuals and sound. It has no dependencies on other modules. See [pyglet.org][1] for the official information. [1]: http://pyglet.org

Section 180.1: Installation of Pyglet Install Python, go into the command line and type: Python 2: pip install pyglet

Python 3: pip3 install pyglet

Section 180.2: Hello World in Pyglet import pyglet window = pyglet.window.Window() label = pyglet.text.Label('Hello, world', font_name='Times New Roman', font_size=36, x=window.width//2, y=window.height//2, anchor_x='center', anchor_y='center') @window.event def on_draw(): window.clear() label.draw() pyglet.app.run()

Section 180.3: Playing Sound in Pyglet sound = pyglet.media.load(sound.wav) sound.play()

Section 180.4: Using Pyglet for OpenGL import pyglet from pyglet.gl import * win = pyglet.window.Window() @win.event() def on_draw(): #OpenGL goes here. Use OpenGL as normal. pyglet.app.run()

Section 180.5: Drawing Points Using Pyglet and OpenGL import pyglet from pyglet.gl import *

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win = pyglet.window.Window() glClear(GL_COLOR_BUFFER_BIT) @win.event def on_draw(): glBegin(GL_POINTS) glVertex2f(x, y) #x is desired distance from left side of window, y is desired distance from bottom of window #make as many vertexes as you want glEnd

To connect the points, replace GL_POINTS with GL_LINE_LOOP.

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Chapter 181: Audio Section 181.1: Working with WAV files winsound Windows environment import winsound winsound.PlaySound("path_to_wav_file.wav", winsound.SND_FILENAME)

wave Support mono/stereo Doesn't support compression/decompression import wave with wave.open("path_to_wav_file.wav", "rb") as wav_file: # Open WAV file in read-only mode. # Get basic information. n_channels = wav_file.getnchannels() # Number of channels. (1=Mono, 2=Stereo). sample_width = wav_file.getsampwidth() # Sample width in bytes. framerate = wav_file.getframerate() # Frame rate. n_frames = wav_file.getnframes() # Number of frames. comp_type = wav_file.getcomptype() # Compression type (only supports "NONE"). comp_name = wav_file.getcompname() # Compression name. # Read audio data. frames = wav_file.readframes(n_frames) # Read n_frames new frames. assert len(frames) == sample_width * n_frames # Duplicate to a new WAV file. with wave.open("path_to_new_wav_file.wav", "wb") as wav_file: # Open WAV file in write-only mode. # Write audio data. params = (n_channels, sample_width, framerate, n_frames, comp_type, comp_name) wav_file.setparams(params) wav_file.writeframes(frames)

Section 181.2: Convert any soundfile with python and mpeg from subprocess import check_call ok = check_call(['ffmpeg','-i','input.mp3','output.wav']) if ok: with open('output.wav', 'rb') as f: wav_file = f.read()

note: http://superuser.com/questions/507386/why-would-i-choose-libav-over-ffmpeg-or-is-there-even-a-difference What are the differences and similarities between ffmpeg, libav, and avconv?

Section 181.3: Playing Windows' beeps Windows provides an explicit interface through which the winsound module allows you to play raw beeps at a given frequency and duration. import winsound

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freq = 2500 # Set frequency To 2500 Hertz dur = 1000 # Set duration To 1000 ms == 1 second winsound.Beep(freq, dur)

Section 181.4: Audio With Pyglet import pyglet audio = pyglet.media.load("audio.wav") audio.play()

For further information, see pyglet

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Chapter 182: pyaudio PyAudio provides Python bindings for PortAudio, the cross-platform audio I/O library. With PyAudio, you can easily use Python to play and record audio on a variety of platforms. PyAudio is inspired by: 1.pyPortAudio/fastaudio: Python bindings for PortAudio v18 API. 2.tkSnack: cross-platform sound toolkit for Tcl/Tk and Python.

Section 182.1: Callback Mode Audio I/O """PyAudio Example: Play a wave file (callback version).""" import import import import

pyaudio wave time sys

if len(sys.argv) < 2: print("Plays a wave file.\n\nUsage: %s filename.wav" % sys.argv[0]) sys.exit(-1) wf = wave.open(sys.argv[1], 'rb') # instantiate PyAudio (1) p = pyaudio.PyAudio() # define callback (2) def callback(in_data, frame_count, time_info, status): data = wf.readframes(frame_count) return (data, pyaudio.paContinue) # open stream using callback (3) stream = p.open(format=p.get_format_from_width(wf.getsampwidth()), channels=wf.getnchannels(), rate=wf.getframerate(), output=True, stream_callback=callback) # start the stream (4) stream.start_stream() # wait for stream to finish (5) while stream.is_active(): time.sleep(0.1) # stop stream (6) stream.stop_stream() stream.close() wf.close() # close PyAudio (7) p.terminate()

In callback mode, PyAudio will call a specified callback function (2) whenever it needs new audio data (to play) and/or when there is new (recorded) audio data available. Note that PyAudio calls the callback function in a separate thread. The function has the following signature callback(, , , ) and must return a tuple containing frame_count frames of audio data and a flag

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signifying whether there are more frames to play/record. Start processing the audio stream using pyaudio.Stream.start_stream() (4), which will call the callback function repeatedly until that function returns pyaudio.paComplete. To keep the stream active, the main thread must not terminate, e.g., by sleeping (5).

Section 182.2: Blocking Mode Audio I/O """PyAudio Example: Play a wave file.""" import pyaudio import wave import sys CHUNK = 1024 if len(sys.argv) < 2: print("Plays a wave file.\n\nUsage: %s filename.wav" % sys.argv[0]) sys.exit(-1) wf = wave.open(sys.argv[1], 'rb') # instantiate PyAudio (1) p = pyaudio.PyAudio() # open stream (2) stream = p.open(format=p.get_format_from_width(wf.getsampwidth()), channels=wf.getnchannels(), rate=wf.getframerate(), output=True) # read data data = wf.readframes(CHUNK) # play stream (3) while len(data) > 0: stream.write(data) data = wf.readframes(CHUNK) # stop stream (4) stream.stop_stream() stream.close() # close PyAudio (5) p.terminate()

To use PyAudio, first instantiate PyAudio using pyaudio.PyAudio() (1), which sets up the portaudio system. To record or play audio, open a stream on the desired device with the desired audio parameters using pyaudio.PyAudio.open() (2). This sets up a pyaudio.Stream to play or record audio. Play audio by writing audio data to the stream using pyaudio.Stream.write(), or read audio data from the stream using pyaudio.Stream.read(). (3) Note that in “blocking mode”, each pyaudio.Stream.write() or pyaudio.Stream.read() blocks until all the given/requested frames have been played/recorded. Alternatively, to generate audio data on the fly or immediately process recorded audio data, use the “callback mode”(refer the example on call back mode) GoalKicker.com – Python® Notes for Professionals

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Use pyaudio.Stream.stop_stream() to pause playing/recording, and pyaudio.Stream.close() to terminate the stream. (4) Finally, terminate the portaudio session using pyaudio.PyAudio.terminate() (5)

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Chapter 183: shelve Shelve is a python module used to store objects in a file. The shelve module implements persistent storage for arbitrary Python objects which can be pickled, using a dictionary-like API. The shelve module can be used as a simple persistent storage option for Python objects when a relational database is overkill. The shelf is accessed by keys, just as with a dictionary. The values are pickled and written to a database created and managed by anydbm.

Section 183.1: Creating a new Shelf The simplest way to use shelve is via the DbfilenameShelf class. It uses anydbm to store the data. You can use the class directly, or simply call shelve.open(): import shelve s = shelve.open('test_shelf.db') try: s['key1'] = { 'int': 10, 'float':9.5, 'string':'Sample data' } finally: s.close()

To access the data again, open the shelf and use it like a dictionary: import shelve s = shelve.open('test_shelf.db') try: existing = s['key1'] finally: s.close() print existing

If you run both sample scripts, you should see: $ python shelve_create.py $ python shelve_existing.py {'int': 10, 'float': 9.5, 'string': 'Sample data'}

The dbm module does not support multiple applications writing to the same database at the same time. If you know your client will not be modifying the shelf, you can tell shelve to open the database read-only. import shelve s = shelve.open('test_shelf.db', flag='r') try: existing = s['key1'] finally: s.close() print existing

If your program tries to modify the database while it is opened read-only, an access error exception is generated. The exception type depends on the database module selected by anydbm when the database was created.

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Section 183.2: Sample code for shelve To shelve an object, first import the module and then assign the object value as follows: import shelve database = shelve.open(filename.suffix) object = Object() database['key'] = object

Section 183.3: To summarize the interface (key is a string, data is an arbitrary object): import shelve d = shelve.open(filename)

# open -- file may get suffix added by low-level # library

d[key] = data

# # # # # #

data = d[key] del d[key]

flag = key in d klist = list(d.keys())

store data at key (overwrites old data if using an existing key) retrieve a COPY of data at key (raise KeyError if no such key) delete data stored at key (raises KeyError if no such key)

# true if the key exists # a list of all existing keys (slow!)

# as d was opened WITHOUT writeback=True, beware: d['xx'] = [0, 1, 2] # this works as expected, but... d['xx'].append(3) # *this doesn't!* -- d['xx'] is STILL [0, 1, 2]! # having opened d without writeback=True, you need to code carefully: temp = d['xx'] # extracts the copy temp.append(5) # mutates the copy d['xx'] = temp # stores the copy right back, to persist it # or, d=shelve.open(filename,writeback=True) would let you just code # d['xx'].append(5) and have it work as expected, BUT it would also # consume more memory and make the d.close() operation slower. d.close()

# close it

Section 183.4: Write-back Shelves do not track modifications to volatile objects, by default. That means if you change the contents of an item stored in the shelf, you must update the shelf explicitly by storing the item again. import shelve s = shelve.open('test_shelf.db') try: print s['key1'] s['key1']['new_value'] = 'this was not here before' finally: s.close() s = shelve.open('test_shelf.db', writeback=True) try:

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print s['key1'] finally: s.close()

In this example, the dictionary at ‘key1’ is not stored again, so when the shelf is re-opened, the changes have not been preserved. $ python shelve_create.py $ python shelve_withoutwriteback.py {'int': 10, 'float': 9.5, 'string': 'Sample data'} {'int': 10, 'float': 9.5, 'string': 'Sample data'}

To automatically catch changes to volatile objects stored in the shelf, open the shelf with writeback enabled. The writeback flag causes the shelf to remember all of the objects retrieved from the database using an in-memory cache. Each cache object is also written back to the database when the shelf is closed. import shelve s = shelve.open('test_shelf.db', writeback=True) try: print s['key1'] s['key1']['new_value'] = 'this was not here before' print s['key1'] finally: s.close() s = shelve.open('test_shelf.db', writeback=True) try: print s['key1'] finally: s.close()

Although it reduces the chance of programmer error, and can make object persistence more transparent, using writeback mode may not be desirable in every situation. The cache consumes extra memory while the shelf is open, and pausing to write every cached object back to the database when it is closed can take extra time. Since there is no way to tell if the cached objects have been modified, they are all written back. If your application reads data more than it writes, writeback will add more overhead than you might want. $ python shelve_create.py $ python shelve_writeback.py {'int': 10, 'float': 9.5, 'string': 'Sample data'} {'int': 10, 'new_value': 'this was not here before', 'float': 9.5, 'string': 'Sample data'} {'int': 10, 'new_value': 'this was not here before', 'float': 9.5, 'string': 'Sample data'}

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Chapter 184: IoT Programming with Python and Raspberry PI Section 184.1: Example - Temperature sensor Interfacing of DS18B20 with Raspberry pi Connection of DS18B20 with Raspberry pi

You can see there are three terminal 1. Vcc 2. Gnd 3. Data (One wire protocol)

R1 is 4.7k ohm resistance for pulling up the voltage level 1. Vcc should be connected to any of the 5v or 3.3v pins of Raspberry pi (PIN : 01, 02, 04, 17). 2. Gnd should be connected to any of the Gnd pins of Raspberry pi (PIN : 06, 09, 14, 20, 25). GoalKicker.com – Python® Notes for Professionals

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3. DATA should be connected to (PIN : 07) Enabling the one-wire interface from the RPi side 4. Login to Raspberry pi using putty or any other linux/unix terminal. 5. After login, open the /boot/config.txt file in your favourite browser. nano /boot/config.txt 6. Now add the this line dtoverlay=w1–gpio to the end of the file. 7. Now reboot the Raspberry pi sudo reboot. 8. Log in to Raspberry pi, and run sudo modprobe g1-gpio 9. Then run sudo modprobe w1-therm 10. Now go to the directory /sys/bus/w1/devices cd /sys/bus/w1/devices 11. Now you will found out a virtual directory created of your temperature sensor starting from 28-********. 12. Go to this directory cd 28-******** 13. Now there is a file name w1-slave, This file contains the temperature and other information like CRC. cat w1-slave.

Now write a module in python to read the temperature import glob import time RATE = 30 sensor_dirs = glob.glob("/sys/bus/w1/devices/28*") if len(sensor_dirs) != 0: while True: time.sleep(RATE) for directories in sensor_dirs: temperature_file = open(directories + "/w1_slave") # Reading the files text = temperature_file.read() temperature_file.close() # Split the text with new lines (\n) and select the second line. second_line = text.split("\n")[1] # Split the line into words, and select the 10th word temperature_data = second_line.split(" ")[9] # We will read after ignoring first two character. temperature = float(temperature_data[2:]) # Now normalise the temperature by dividing 1000. temperature = temperature / 1000 print 'Address : '+str(directories.split('/')[-1])+', Temperature : '+str(temperature)

Above python module will print the temperature vs address for infinite time. RATE parameter is defined to change or adjust the frequency of temperature query from the sensor. GPIO pin diagram 1. [https://www.element14.com/community/servlet/JiveServlet/previewBody/73950-102-11-339300/pi3_gpio.pn GoalKicker.com – Python® Notes for Professionals

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g][3]

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VIDEO: Complete Python Bootcamp: Go from zero to hero in Python 3 Learn Python like a Professional! Start from the basics and go all the way to creating your own applications and games!

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Chapter 185: kivy - Cross-platform Python Framework for NUI Development NUI : A natural user interface (NUI) is a system for human-computer interaction that the user operates through intuitive actions related to natural, everyday human behavior. Kivy is a Python library for development of multi-touch enabled media rich applications which can be installed on different devices. Multi-touch refers to the ability of a touch-sensing surface (usually a touch screen or a trackpad) to detect or sense input from two or more points of contact simultaneously.

Section 185.1: First App To create an kivy application 1. sub class the app class 2. Implement the build method, which will return the widget. 3. Instantiate the class an invoke the run. from kivy.app import App from kivy.uix.label import Label class Test(App): def build(self): return Label(text='Hello world') if __name__ == '__main__': Test().run()

Explanation from kivy.app import App

The above statement will import the parent class app. This will be present in your installation directory your_installtion_directory/kivy/app.py from kivy.uix.label import Label

The above statement will import the ux element Label. All the ux element are present in your installation directory your_installation_directory/kivy/uix/. class Test(App):

The above statement is for to create your app and class name will be your app name. This class is inherited the parent app class. def build(self):

The above statement override the build method of app class. Which will return the widget that needs to be shown when you will start the app. return Label(text='Hello world')

The above statement is the body of the build method. It is returning the Label with its text Hello world. GoalKicker.com – Python® Notes for Professionals

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if __name__ == '__main__':

The above statement is the entry point from where python interpreter start executing your app. Test().run()

The above statement Initialise your Test class by creating its instance. And invoke the app class function run(). Your app will look like the below picture.

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Chapter 186: Pandas Transform: Preform operations on groups and concatenate the results Section 186.1: Simple transform First, Let's create a dummy dataframe We assume that a customer can have n orders, an order can have m items, and items can be ordered more multiple times orders_df = pd.DataFrame() orders_df['customer_id'] = [1,1,1,1,1,2,2,3,3,3,3,3] orders_df['order_id'] = [1,1,1,2,2,3,3,4,5,6,6,6] orders_df['item'] = ['apples', 'chocolate', 'chocolate', 'coffee', 'coffee', 'apples', 'bananas', 'coffee', 'milkshake', 'chocolate', 'strawberry', 'strawberry'] # And this is how the dataframe looks like: print(orders_df) # customer_id order_id item # 0 1 1 apples # 1 1 1 chocolate # 2 1 1 chocolate # 3 1 2 coffee # 4 1 2 coffee # 5 2 3 apples # 6 2 3 bananas # 7 3 4 coffee # 8 3 5 milkshake # 9 3 6 chocolate # 10 3 6 strawberry # 11 3 6 strawberry

. . Now, we will use pandas transform function to count the number of orders per customer # First, we define the function that will be applied per customer_id count_number_of_orders = lambda x: len(x.unique()) # And now, we can transform each group using the logic defined above orders_df['number_of_orders_per_cient'] = ( # Put the results into a new column that is called 'number_of_orders_per_cient' orders_df # Take the original dataframe .groupby(['customer_id'])['order_id'] # Create a separate group for each customer_id & select the order_id .transform(count_number_of_orders)) # Apply the function to each group separately # Inspecting the results ... print(orders_df) # customer_id order_id # 0 1 1 # 1 1 1 # 2 1 1 # 3 1 2 # 4 1 2

item apples chocolate chocolate coffee coffee

number_of_orders_per_cient 2 2 2 2 2

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# # # # # # #

5 6 7 8 9 10 11

2 2 3 3 3 3 3

3 3 4 5 6 6 6

apples bananas coffee milkshake chocolate strawberry strawberry

1 1 3 3 3 3 3

Section 186.2: Multiple results per group Using transform functions that return sub-calculations per group In the previous example, we had one result per client. However, functions returning different values for the group can also be applied. # Create a dummy dataframe orders_df = pd.DataFrame() orders_df['customer_id'] = [1,1,1,1,1,2,2,3,3,3,3,3] orders_df['order_id'] = [1,1,1,2,2,3,3,4,5,6,6,6] orders_df['item'] = ['apples', 'chocolate', 'chocolate', 'coffee', 'coffee', 'apples', 'bananas', 'coffee', 'milkshake', 'chocolate', 'strawberry', 'strawberry']

# Let's try to see if the items were ordered more than once in each orders # First, we define a function that will be applied per group def multiple_items_per_order(_items): # Apply .duplicated, which will return True is the item occurs more than once. multiple_item_bool = _items.duplicated(keep=False) return(multiple_item_bool) # Then, we transform each group according to the defined function orders_df['item_duplicated_per_order'] = ( # Put the results into a new column orders_df # Take the orders dataframe .groupby(['order_id'])['item'] # Create a separate group for each order_id & select the item .transform(multiple_items_per_order)) # Apply the defined function to each group separately # Inspecting the results ... print(orders_df) # customer_id order_id item # 0 1 1 apples # 1 1 1 chocolate # 2 1 1 chocolate # 3 1 2 coffee # 4 1 2 coffee # 5 2 3 apples # 6 2 3 bananas # 7 3 4 coffee # 8 3 5 milkshake # 9 3 6 chocolate # 10 3 6 strawberry # 11 3 6 strawberry

item_duplicated_per_order False True True True True False False False False False True True

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Chapter 187: Similarities in syntax, Dierences in meaning: Python vs. JavaScript It sometimes happens that two languages put different meanings on the same or similar syntax expression. When the both languages are of interest for a programmer, clarifying these bifurcation points helps to better understand the both languages in their basics and subtleties.

Section 187.1: `in` with lists 2 in [2, 3]

In Python this evaluates to True, but in JavaScript to false. This is because in Python in checks if a value is contained in a list, so 2 is in [2, 3] as its first element. In JavaScript in is used with objects and checks if an object contains the property with the name expressed by the value. So JavaScript considers [2, 3] as an object or a key-value map like this: {'0': 2, '1': 3}

and checks if it has a property or a key '2' in it. Integer 2 is silently converted to string '2'.

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Chapter 188: Call Python from C# The documentation provides a sample implementation of the inter-process communication between C# and Python scripts.

Section 188.1: Python script to be called by C# application import sys import json # load input arguments from the text file filename = sys.argv[ 1 ] with open( filename ) as data_file: input_args = json.loads( data_file.read() ) # cast strings to floats x, y = [ float(input_args.get( key )) for key in [ 'x', 'y' ] ] print json.dumps( { 'sum' : x + y , 'subtract' : x - y } )

Section 188.2: C# code calling Python script using using using using

MongoDB.Bson; System; System.Diagnostics; System.IO;

namespace python_csharp { class Program { static void Main(string[] args) { // full path to .py file string pyScriptPath = "...../sum.py"; // convert input arguments to JSON string BsonDocument argsBson = BsonDocument.Parse("{ 'x' : '1', 'y' : '2' }"); bool saveInputFile = false; string argsFile = string.Format("{0}\\{1}.txt", Path.GetDirectoryName(pyScriptPath), Guid.NewGuid()); string outputString = null; // create new process start info ProcessStartInfo prcStartInfo = new ProcessStartInfo { // full path of the Python interpreter 'python.exe' FileName = "python.exe", // string.Format(@"""{0}""", "python.exe"), UseShellExecute = false, RedirectStandardOutput = true, CreateNoWindow = false }; try { // write input arguments to .txt file using (StreamWriter sw = new StreamWriter(argsFile))

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{ sw.WriteLine(argsBson); prcStartInfo.Arguments = string.Format("{0} {1}", string.Format(@"""{0}""", pyScriptPath), string.Format(@"""{0}""", argsFile)); } // start process using (Process process = Process.Start(prcStartInfo)) { // read standard output JSON string using (StreamReader myStreamReader = process.StandardOutput) { outputString = myStreamReader.ReadLine(); process.WaitForExit(); } } } finally { // delete/save temporary .txt file if (!saveInputFile) { File.Delete(argsFile); } } Console.WriteLine(outputString); } } }

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Chapter 189: ctypes ctypes is a python built-in library that invokes exported functions from native compiled libraries.

Note: Since this library handles compiled code, it is relatively OS dependent.

Section 189.1: ctypes arrays As any good C programmer knows, a single value won't get you that far. What will really get us going are arrays! >>> c_int * 16

This is not an actual array, but it's pretty darn close! We created a class that denotes an array of 16 ints. Now all we need to do is to initialize it: >>> arr = (c_int * 16)(*range(16)) >>> arr

Now arr is an actual array that contains the numbers from 0 to 15. They can be accessed just like any list: >>> arr[5] 5 >>> arr[5] = 20 >>> arr[5] 20

And just like any other ctypes object, it also has a size and a location: >>> sizeof(arr) 64 # sizeof(c_int) * 16 >>> hex(addressof(arr)) '0xc000l0ff'

Section 189.2: Wrapping functions for ctypes In some cases, a C function accepts a function pointer. As avid ctypes users, we would like to use those functions, and even pass python function as arguments. Let's define a function: >>> def max(x, y): return x if x >= y else y

Now, that function takes two arguments and returns a result of the same type. For the sake of the example, let's assume that type is an int. Like we did on the array example, we can define an object that denotes that prototype: >>> CFUNCTYPE(c_int, c_int, c_int)

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That prototype denotes a function that returns an c_int (the first argument), and accepts two c_int arguments (the other arguments). Now let's wrap the function: >>> CFUNCTYPE(c_int, c_int, c_int)(max)

Function prototypes have on more usage: They can wrap ctypes function (like libc.ntohl) and verify that the correct arguments are used when invoking the function. >>> libc.ntohl() # garbage in - garbage out >>> CFUNCTYPE(c_int, c_int)(libc.ntohl)() Traceback (most recent call last): File "", line 1, in TypeError: this function takes at least 1 argument (0 given)

Section 189.3: Basic usage Let's say we want to use libc's ntohl function. First, we must load libc.so: >>> from ctypes import * >>> libc = cdll.LoadLibrary('libc.so.6') >>> libc

Then, we get the function object: >>> ntohl = libc.ntohl >>> ntohl

And now, we can simply invoke the function: >>> ntohl(0x6C) 1811939328 >>> hex(_) '0x6c000000'

Which does exactly what we expect it to do.

Section 189.4: Common pitfalls Failing to load a file The first possible error is failing to load the library. In that case an OSError is usually raised. This is either because the file doesn't exists (or can't be found by the OS): >>> cdll.LoadLibrary("foobar.so") Traceback (most recent call last): File "", line 1, in

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File "/usr/lib/python3.5/ctypes/__init__.py", line 425, in LoadLibrary return self._dlltype(name) File "/usr/lib/python3.5/ctypes/__init__.py", line 347, in __init__ self._handle = _dlopen(self._name, mode) OSError: foobar.so: cannot open shared object file: No such file or directory

As you can see, the error is clear and pretty indicative. The second reason is that the file is found, but is not of the correct format. >>> cdll.LoadLibrary("libc.so") Traceback (most recent call last): File "", line 1, in File "/usr/lib/python3.5/ctypes/__init__.py", line 425, in LoadLibrary return self._dlltype(name) File "/usr/lib/python3.5/ctypes/__init__.py", line 347, in __init__ self._handle = _dlopen(self._name, mode) OSError: /usr/lib/i386-linux-gnu/libc.so: invalid ELF header

In this case, the file is a script file and not a .so file. This might also happen when trying to open a .dll file on a Linux machine or a 64bit file on a 32bit python interpreter. As you can see, in this case the error is a bit more vague, and requires some digging around. Failing to access a function Assuming we successfully loaded the .so file, we then need to access our function like we've done on the first example. When a non-existing function is used, an AttributeError is raised: >>> libc.foo Traceback (most recent call last): File "", line 1, in File "/usr/lib/python3.5/ctypes/__init__.py", line 360, in __getattr__ func = self.__getitem__(name) File "/usr/lib/python3.5/ctypes/__init__.py", line 365, in __getitem__ func = self._FuncPtr((name_or_ordinal, self)) AttributeError: /lib/i386-linux-gnu/libc.so.6: undefined symbol: foo

Section 189.5: Basic ctypes object The most basic object is an int: >>> obj = ctypes.c_int(12) >>> obj c_long(12)

Now, obj refers to a chunk of memory containing the value 12. That value can be accessed directly, and even modified: >>> obj.value 12 >>> obj.value = 13 >>> obj c_long(13)

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Since obj refers to a chunk of memory, we can also find out its size and location: >>> sizeof(obj) 4 >>> hex(addressof(obj)) '0xdeadbeef'

Section 189.6: Complex usage Let's combine all of the examples above into one complex scenario: using libc's lfind function. For more details about the function, read the man page. I urge you to read it before going on. First, we'll define the proper prototypes: >>> compar_proto = CFUNCTYPE(c_int, POINTER(c_int), POINTER(c_int)) >>> lfind_proto = CFUNCTYPE(c_void_p, c_void_p, c_void_p, POINTER(c_uint), c_uint, compar_proto)

Then, let's create the variables: >>> key = c_int(12) >>> arr = (c_int * 16)(*range(16)) >>> nmemb = c_uint(16)

And now we define the comparison function: >>> def compar(x, y): return x.contents.value - y.contents.value

Notice that x, and y are POINTER(c_int), so we need to dereference them and take their values in order to actually compare the value stored in the memory. Now we can combine everything together: >>> lfind = lfind_proto(libc.lfind) >>> ptr = lfind(byref(key), byref(arr), byref(nmemb), sizeof(c_int), compar_proto(compar)) ptr is the returned void pointer. If key wasn't found in arr, the value would be None, but in this case we got a valid

value. Now we can convert it and access the value: >>> cast(ptr, POINTER(c_int)).contents c_long(12)

Also, we can see that ptr points to the correct value inside arr: >>> addressof(arr) + 12 * sizeof(c_int) == ptr True

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VIDEO: Python for Data Science and Machine Learning Bootcamp Learn how to use NumPy, Pandas, Seaborn, Matplotlib , Plotly, Scikit-Learn , Machine Learning, Tensorflow, and more!

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Chapter 190: Writing extensions Section 190.1: Hello World with C Extension The following C source file (which we will call hello.c for demonstration purposes) produces an extension module named hello that contains a single function greet(): #include #include #if PY_MAJOR_VERSION >= 3 #define IS_PY3K #endif static PyObject *hello_greet(PyObject *self, PyObject *args) { const char *input; if (!PyArg_ParseTuple(args, "s", &input)) { return NULL; } printf("%s", input); Py_RETURN_NONE; } static PyMethodDef HelloMethods[] = { { "greet", hello_greet, METH_VARARGS, "Greet the user" }, { NULL, NULL, 0, NULL } }; #ifdef IS_PY3K static struct PyModuleDef hellomodule = { PyModuleDef_HEAD_INIT, "hello", NULL, -1, HelloMethods }; PyMODINIT_FUNC PyInit_hello(void) { return PyModule_Create(&hellomodule); } #else PyMODINIT_FUNC inithello(void) { (void) Py_InitModule("hello", HelloMethods); } #endif

To compile the file with the gcc compiler, run the following command in your favourite terminal: gcc /path/to/your/file/hello.c -o /path/to/your/file/hello

To execute the greet() function that we wrote earlier, create a file in the same directory, and call it hello.py import hello # imports the compiled library hello.greet("Hello!") # runs the greet() function with "Hello!" as an argument

Section 190.2: C Extension Using c++ and Boost This is a basic example of a C Extension using C++ and Boost.

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C++ Code C++ code put in hello.cpp: #include #include #include #include



// Return a hello world string. std::string get_hello_function() { return "Hello world!"; } // hello class that can return a list of count hello world strings. class hello_class { public: // Taking the greeting message in the constructor. hello_class(std::string message) : _message(message) {} // Returns the message count times in a python list. boost::python::list as_list(int count) { boost::python::list res; for (int i = 0; i < count; ++i) { res.append(_message); } return res; } private: std::string _message; };

// Defining a python module naming it to "hello". BOOST_PYTHON_MODULE(hello) { // Here you declare what functions and classes that should be exposed on the module. // The get_hello_function exposed to python as a function. boost::python::def("get_hello", get_hello_function); // The hello_class exposed to python as a class. boost::python::class_("Hello", boost::python::init()) .def("as_list", &hello_class::as_list) ; }

To compile this into a python module you will need the python headers and the boost libraries. This example was made on Ubuntu 12.04 using python 3.4 and gcc. Boost is supported on many platforms. In case of Ubuntu the needed packages was installed using: sudo apt-get install gcc libboost-dev libpython3.4-dev

Compiling the source file into a .so-file that can later be imported as a module provided it is on the python path:

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gcc -shared -o hello.so -fPIC -I/usr/include/python3.4 hello.cpp -lboost_python-py34 -lboost_system -l:libpython3.4m.so

The python code in the file example.py: import hello print(hello.get_hello()) h = hello.Hello("World hello!") print(h.as_list(3))

Then python3 example.py will give the following output: Hello world! ['World hello!', 'World hello!', 'World hello!']

Section 190.3: Passing an open file to C Extensions Pass an open file object from Python to C extension code. You can convert the file to an integer file descriptor using PyObject_AsFileDescriptor function: PyObject *fobj; int fd = PyObject_AsFileDescriptor(fobj); if (fd < 0){ return NULL; }

To convert an integer file descriptor back into a python object, use PyFile_FromFd. int fd; /* Existing file descriptor */ PyObject *fobj = PyFile_FromFd(fd, "filename","r",-1,NULL,NULL,NULL,1);

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Chapter 191: Python Lex-Yacc PLY is a pure-Python implementation of the popular compiler construction tools lex and yacc.

Section 191.1: Getting Started with PLY To install PLY on your machine for python2/3, follow the steps outlined below: 1. Download the source code from here. 2. Unzip the downloaded zip file 3. Navigate into the unzipped ply-3.10 folder 4. Run the following command in your terminal: python setup.py install If you completed all the above, you should now be able to use the PLY module. You can test it out by opening a python interpreter and typing import ply.lex. Note: Do not use pip to install PLY, it will install a broken distribution on your machine.

Section 191.2: The "Hello, World!" of PLY - A Simple Calculator Let's demonstrate the power of PLY with a simple example: this program will take an arithmetic expression as a string input, and attempt to solve it. Open up your favourite editor and copy the following code: from ply import lex import ply.yacc as yacc tokens = ( 'PLUS', 'MINUS', 'TIMES', 'DIV', 'LPAREN', 'RPAREN', 'NUMBER', ) t_ignore = ' \t' t_PLUS t_MINUS t_TIMES t_DIV t_LPAREN t_RPAREN

= = = = = =

r'\+' r'-' r'\*' r'/' r'\(' r'\)'

def t_NUMBER( t ) : r'[0-9]+' t.value = int( t.value ) return t def t_newline( t ): r'\n+' t.lexer.lineno += len( t.value ) def t_error( t ):

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print("Invalid Token:",t.value[0]) t.lexer.skip( 1 ) lexer = lex.lex() precedence = ( ( 'left', 'PLUS', 'MINUS' ), ( 'left', 'TIMES', 'DIV' ), ( 'nonassoc', 'UMINUS' ) ) def p_add( p ) : 'expr : expr PLUS expr' p[0] = p[1] + p[3] def p_sub( p ) : 'expr : expr MINUS expr' p[0] = p[1] - p[3] def p_expr2uminus( p ) : 'expr : MINUS expr %prec UMINUS' p[0] = - p[2] def p_mult_div( p ) : '''expr : expr TIMES expr | expr DIV expr''' if p[2] == '*' : p[0] = p[1] * p[3] else : if p[3] == 0 : print("Can't divide by 0") raise ZeroDivisionError('integer division by 0') p[0] = p[1] / p[3] def p_expr2NUM( p ) : 'expr : NUMBER' p[0] = p[1] def p_parens( p ) : 'expr : LPAREN expr RPAREN' p[0] = p[2] def p_error( p ): print("Syntax error in input!") parser = yacc.yacc() res = parser.parse("-4*-(3-5)") # the input print(res)

Save this file as calc.py and run it. Output: -8

Which is the right answer for -4 * - (3 - 5).

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Section 191.3: Part 1: Tokenizing Input with Lex There are two steps that the code from example 1 carried out: one was tokenizing the input, which means it looked for symbols that constitute the arithmetic expression, and the second step was parsing, which involves analysing the extracted tokens and evaluating the result. This section provides a simple example of how to tokenize user input, and then breaks it down line by line. import ply.lex as lex # List of token names. This is always required tokens = [ 'NUMBER', 'PLUS', 'MINUS', 'TIMES', 'DIVIDE', 'LPAREN', 'RPAREN', ] # Regular t_PLUS t_MINUS t_TIMES t_DIVIDE t_LPAREN t_RPAREN

expression rules for simple tokens = r'\+' = r'-' = r'\*' = r'/' = r'\(' = r'\)'

# A regular expression rule with some action code def t_NUMBER(t): r'\d+' t.value = int(t.value) return t # Define a rule so we can track line numbers def t_newline(t): r'\n+' t.lexer.lineno += len(t.value) # A string containing ignored characters (spaces and tabs) t_ignore = ' \t' # Error handling rule def t_error(t): print("Illegal character '%s'" % t.value[0]) t.lexer.skip(1) # Build the lexer lexer = lex.lex() # Give the lexer some input lexer.input(data) # Tokenize while True: tok = lexer.token() if not tok: break # No more input print(tok)

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Save this file as calclex.py. We'll be using this when building our Yacc parser.

Breakdown 1. Import the module using import ply.lex 2. All lexers must provide a list called tokens that defines all of the possible token names that can be produced by the lexer. This list is always required. tokens = [ 'NUMBER', 'PLUS', 'MINUS', 'TIMES', 'DIVIDE', 'LPAREN', 'RPAREN', ]

tokens could also be a tuple of strings (rather than a string), where each string denotes a token as before.

3. The regex rule for each string may be defined either as a string or as a function. In either case, the variable name should be prefixed by t_ to denote it is a rule for matching tokens. For simple tokens, the regular expression can be specified as strings: t_PLUS = r'\+' If some kind of action needs to be performed, a token rule can be specified as a function. def t_NUMBER(t): r'\d+' t.value = int(t.value) return t

Note, the rule is specified as a doc string within the function. The function accepts one argument which is an instance of LexToken, performs some action and then returns back the argument. If you want to use an external string as the regex rule for the function instead of specifying a doc string, consider the following example: @TOKEN(identifier) def t_ID(t): ... # actions

# identifier is a string holding the regex

An instance of LexToken object (let's call this object t) has the following attributes: 1. t.type which is the token type (as a string) (eg: 'NUMBER', 'PLUS', etc). By default, t.type is set to the name following the t_ prefix. 2. t.value which is the lexeme (the actual text matched) 3. t.lineno which is the current line number (this is not automatically updated, as the lexer knows nothing of line numbers). Update lineno using a function called t_newline. def t_newline(t): r'\n+' t.lexer.lineno += len(t.value)

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4. t.lexpos which is the position of the token relative to the beginning of the input text. If nothing is returned from a regex rule function, the token is discarded. If you want to discard a token, you can alternatively add t_ignore_ prefix to a regex rule variable instead of defining a function for the same rule. def t_COMMENT(t): r'\#.*' pass # No return value. Token discarded

...Is the same as: t_ignore_COMMENT = r'\#.*'

This is of course invalid if you're carrying out some action when you see a comment. In which case, use a function to define the regex rule. If you haven't defined a token for some characters but still want to ignore it, use t_ignore = "" (these prefixes are necessary): t_ignore_COMMENT = r'\#.*' t_ignore = ' \t' # ignores spaces and tabs

When building the master regex, lex will add the regexes specified in the file as follows: 1. Tokens defined by functions are added in the same order as they appear in the file. 2. Tokens defined by strings are added in decreasing order of the string length of the string defining the regex for that token. If you are matching == and = in the same file, take advantage of these rules.

Literals are tokens that are returned as they are. Both t.type and t.value will be set to the character itself. Define a list of literals as such: literals = [ '+', '-', '*', '/' ]

or, literals = "+-*/"

It is possible to write token functions that perform additional actions when literals are matched. However, you'll need to set the token type appropriately. For example: literals = [ '{', '}' ] def t_lbrace(t): r'\{' t.type = '{' literal) return t

# Set token type to the expected literal (ABSOLUTE MUST if this is a

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Handle errors with t_error function. # Error handling rule def t_error(t): print("Illegal character '%s'" % t.value[0]) t.lexer.skip(1) # skip the illegal token (don't process it)

In general, t.lexer.skip(n) skips n characters in the input string. 4. Final preparations: Build the lexer using lexer = lex.lex(). You can also put everything inside a class and call use instance of the class to define the lexer. Eg:

import ply.lex as lex class MyLexer(object): ... # everything relating to token rules and error handling comes here as usual # Build the lexer def build(self, **kwargs): self.lexer = lex.lex(module=self, **kwargs) def test(self, data): self.lexer.input(data) for token in self.lexer.token(): print(token) # Build the lexer and try it out m = MyLexer() m.build() m.test("3 + 4")

# Build the lexer #

Provide input using lexer.input(data) where data is a string To get the tokens, use lexer.token() which returns tokens matched. You can iterate over lexer in a loop as in:

for i in lexer: print(i)

Section 191.4: Part 2: Parsing Tokenized Input with Yacc This section explains how the tokenized input from Part 1 is processed - it is done using Context Free Grammars (CFGs). The grammar must be specified, and the tokens are processed according to the grammar. Under the hood, the parser uses an LALR parser. # Yacc example import ply.yacc as yacc # Get the token map from the lexer. This is required. from calclex import tokens

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def p_expression_plus(p): 'expression : expression PLUS term' p[0] = p[1] + p[3] def p_expression_minus(p): 'expression : expression MINUS term' p[0] = p[1] - p[3] def p_expression_term(p): 'expression : term' p[0] = p[1] def p_term_times(p): 'term : term TIMES factor' p[0] = p[1] * p[3] def p_term_div(p): 'term : term DIVIDE factor' p[0] = p[1] / p[3] def p_term_factor(p): 'term : factor' p[0] = p[1] def p_factor_num(p): 'factor : NUMBER' p[0] = p[1] def p_factor_expr(p): 'factor : LPAREN expression RPAREN' p[0] = p[2] # Error rule for syntax errors def p_error(p): print("Syntax error in input!") # Build the parser parser = yacc.yacc() while True: try: s = raw_input('calc > ') except EOFError: break if not s: continue result = parser.parse(s) print(result)

Breakdown Each grammar rule is defined by a function where the docstring to that function contains the appropriate context-free grammar specification. The statements that make up the function body implement the semantic actions of the rule. Each function accepts a single argument p that is a sequence containing the values of each grammar symbol in the corresponding rule. The values of p[i] are mapped to grammar symbols as shown here: def p_expression_plus(p): 'expression : expression PLUS term' # ^ ^ ^ ^ # p[0] p[1] p[2] p[3]

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p[0] = p[1] + p[3]

For tokens, the "value" of the corresponding p[i] is the same as the p.value attribute assigned in the lexer module. So, PLUS will have the value +. For non-terminals, the value is determined by whatever is placed in p[0]. If nothing is placed, the value is None. Also, p[-1] is not the same as p[3], since p is not a simple list (p[-1] can specify embedded actions (not discussed here)). Note that the function can have any name, as long as it is preceded by p_. The p_error(p) rule is defined to catch syntax errors (same as yyerror in yacc/bison). Multiple grammar rules can be combined into a single function, which is a good idea if productions have a similar structure. def p_binary_operators(p): '''expression : expression PLUS term | expression MINUS term term : term TIMES factor | term DIVIDE factor''' if p[2] == '+': p[0] = p[1] + p[3] elif p[2] == '-': p[0] = p[1] - p[3] elif p[2] == '*': p[0] = p[1] * p[3] elif p[2] == '/': p[0] = p[1] / p[3]

Character literals can be used instead of tokens. def p_binary_operators(p): '''expression : expression '+' term | expression '-' term term : term '*' factor | term '/' factor''' if p[2] == '+': p[0] = p[1] + p[3] elif p[2] == '-': p[0] = p[1] - p[3] elif p[2] == '*': p[0] = p[1] * p[3] elif p[2] == '/': p[0] = p[1] / p[3]

Of course, the literals must be specified in the lexer module. Empty productions have the form '''symbol : ''' To explicitly set the start symbol, use start = 'foo', where foo is some non-terminal. Setting precedence and associativity can be done using the precedence variable.

precedence = ( ('nonassoc', 'LESSTHAN', 'GREATERTHAN'),

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('left', 'PLUS', 'MINUS'), ('left', 'TIMES', 'DIVIDE'), ('right', 'UMINUS'),

# Unary minus operator

)

Tokens are ordered from lowest to highest precedence. nonassoc means that those tokens do not associate. This means that something like a < b < c is illegal whereas a < b is still legal. parser.out is a debugging file that is created when the yacc program is executed for the first time. Whenever

a shift/reduce conflict occurs, the parser always shifts.

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Chapter 192: Unit Testing Section 192.1: Test Setup and Teardown within a unittest.TestCase Sometimes we want to prepare a context for each test to be run under. The setUp method is run prior to each test in the class. tearDown is run at the end of every test. These methods are optional. Remember that TestCases are often used in cooperative multiple inheritance so you should be careful to always call super in these methods so that base class's setUp and tearDown methods also get called. The base implementation of TestCase provides empty setUp and tearDown methods so that they can be called without raising exceptions: import unittest

class SomeTest(unittest.TestCase): def setUp(self): super(SomeTest, self).setUp() self.mock_data = [1,2,3,4,5] def test(self): self.assertEqual(len(self.mock_data), 5) def tearDown(self): super(SomeTest, self).tearDown() self.mock_data = []

if __name__ == '__main__': unittest.main()

Note that in python2.7+, there is also the addCleanup method that registers functions to be called after the test is run. In contrast to tearDown which only gets called if setUp succeeds, functions registered via addCleanup will be called even in the event of an unhandled exception in setUp. As a concrete example, this method can frequently be seen removing various mocks that were registered while the test was running: import unittest import some_module

class SomeOtherTest(unittest.TestCase): def setUp(self): super(SomeOtherTest, self).setUp() # Replace `some_module.method` with a `mock.Mock` my_patch = mock.patch.object(some_module, 'method') my_patch.start() # When the test finishes running, put the original method back. self.addCleanup(my_patch.stop)

Another benefit of registering cleanups this way is that it allows the programmer to put the cleanup code next to the setup code and it protects you in the event that a subclasser forgets to call super in tearDown.

Section 192.2: Asserting on Exceptions You can test that a function throws an exception with the built-in unittest through two different methods. GoalKicker.com – Python® Notes for Professionals

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Using a context manager def division_function(dividend, divisor): return dividend / divisor

class MyTestCase(unittest.TestCase): def test_using_context_manager(self): with self.assertRaises(ZeroDivisionError): x = division_function(1, 0)

This will run the code inside of the context manager and, if it succeeds, it will fail the test because the exception was not raised. If the code raises an exception of the correct type, the test will continue. You can also get the content of the raised exception if you want to execute additional assertions against it. class MyTestCase(unittest.TestCase): def test_using_context_manager(self): with self.assertRaises(ZeroDivisionError) as ex: x = division_function(1, 0) self.assertEqual(ex.message, 'integer division or modulo by zero')

By providing a callable function def division_function(dividend, divisor): """ Dividing two numbers. :type dividend: int :type divisor: int :raises: ZeroDivisionError if divisor is zero (0). :rtype: int """ return dividend / divisor

class MyTestCase(unittest.TestCase): def test_passing_function(self): self.assertRaises(ZeroDivisionError, division_function, 1, 0)

The exception to check for must be the first parameter, and a callable function must be passed as the second parameter. Any other parameters specified will be passed directly to the function that is being called, allowing you to specify the parameters that trigger the exception.

Section 192.3: Testing Exceptions Programs throw errors when for instance wrong input is given. Because of this, one needs to make sure that an error is thrown when actual wrong input is given. Because of that we need to check for an exact exception, for this example we will use the following exception: class WrongInputException(Exception): pass

This exception is raised when wrong input is given, in the following context where we always expect a number as text input. GoalKicker.com – Python® Notes for Professionals

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def convert2number(random_input): try: my_input = int(random_input) except ValueError: raise WrongInputException("Expected an integer!") return my_input

To check whether an exception has been raised, we use assertRaises to check for that exception. assertRaises can be used in two ways: 1. Using the regular function call. The first argument takes the exception type, second a callable (usually a function) and the rest of arguments are passed to this callable. 2. Using a with clause, giving only the exception type to the function. This has as advantage that more code can be executed, but should be used with care since multiple functions can use the same exception which can be problematic. An example: with self.assertRaises(WrongInputException): convert2number("not a number") This first has been implemented in the following test case: import unittest class ExceptionTestCase(unittest.TestCase): def test_wrong_input_string(self): self.assertRaises(WrongInputException, convert2number, "not a number") def test_correct_input(self): try: result = convert2number("56") self.assertIsInstance(result, int) except WrongInputException: self.fail()

There also may be a need to check for an exception which should not have been thrown. However, a test will automatically fail when an exception is thrown and thus may not be necessary at all. Just to show the options, the second test method shows a case on how one can check for an exception not to be thrown. Basically, this is catching the exception and then failing the test using the fail method.

Section 192.4: Choosing Assertions Within Unittests While Python has an assert statement, the Python unit testing framework has better assertions specialized for tests: they are more informative on failures, and do not depend on the execution's debug mode. Perhaps the simplest assertion is assertTrue, which can be used like this: import unittest class SimplisticTest(unittest.TestCase): def test_basic(self): self.assertTrue(1 + 1 == 2)

This will run fine, but replacing the line above with self.assertTrue(1 + 1 == 3)

will fail.

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The assertTrue assertion is quite likely the most general assertion, as anything tested can be cast as some boolean condition, but often there are better alternatives. When testing for equality, as above, it is better to write self.assertEqual(1 + 1, 3)

When the former fails, the message is ====================================================================== FAIL: test (__main__.TruthTest) ---------------------------------------------------------------------Traceback (most recent call last): File "stuff.py", line 6, in test self.assertTrue(1 + 1 == 3) AssertionError: False is not true

but when the latter fails, the message is ====================================================================== FAIL: test (__main__.TruthTest) ---------------------------------------------------------------------Traceback (most recent call last): File "stuff.py", line 6, in test self.assertEqual(1 + 1, 3) AssertionError: 2 != 3

which is more informative (it actually evaluated the result of the left hand side). You can find the list of assertions in the standard documentation. In general, it is a good idea to choose the assertion that is the most specifically fitting the condition. Thus, as shown above, for asserting that 1 + 1 == 2 it is better to use assertEqual than assertTrue. Similarly, for asserting that a is None, it is better to use assertIsNone than assertEqual. Note also that the assertions have negative forms. Thus assertEqual has its negative counterpart assertNotEqual, and assertIsNone has its negative counterpart assertIsNotNone. Once again, using the negative counterparts when appropriate, will lead to clearer error messages.

Section 192.5: Unit tests with pytest installing pytest: pip install pytest

getting the tests ready:

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mkdir tests touch tests/test_docker.py

Functions to test in docker_something/helpers.py: from subprocess import Popen, PIPE # this Popen is monkeypatched with the fixture `all_popens` def copy_file_to_docker(src, dest): try: result = Popen(['docker','cp', src, 'something_cont:{}'.format(dest)], stdout=PIPE, stderr=PIPE) err = result.stderr.read() if err: raise Exception(err) except Exception as e: print(e) return result def docker_exec_something(something_file_string): fl = Popen(["docker", "exec", "-i", "something_cont", "something"], stdin=PIPE, stdout=PIPE, stderr=PIPE) fl.stdin.write(something_file_string) fl.stdin.close() err = fl.stderr.read() fl.stderr.close() if err: print(err) exit() result = fl.stdout.read() print(result)

The test imports test_docker.py: import os from tempfile import NamedTemporaryFile import pytest from subprocess import Popen, PIPE from docker_something import helpers copy_file_to_docker = helpers.copy_file_to_docker docker_exec_something = helpers.docker_exec_something

mocking a file like object in test_docker.py: class MockBytes(): '''Used to collect bytes ''' all_read = [] all_write = [] all_close = [] def read(self, *args, **kwargs): # print('read', args, kwargs, dir(self)) self.all_read.append((self, args, kwargs)) def write(self, *args, **kwargs): # print('wrote', args, kwargs) self.all_write.append((self, args, kwargs))

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def close(self, *args, **kwargs): # print('closed', self, args, kwargs) self.all_close.append((self, args, kwargs)) def get_all_mock_bytes(self): return self.all_read, self.all_write, self.all_close

Monkey patching with pytest in test_docker.py: @pytest.fixture def all_popens(monkeypatch): '''This fixture overrides / mocks the builtin Popen and replaces stdin, stdout, stderr with a MockBytes object note: monkeypatch is magically imported ''' all_popens = [] class MockPopen(object): def __init__(self, args, stdout=None, stdin=None, stderr=None): all_popens.append(self) self.args = args self.byte_collection = MockBytes() self.stdin = self.byte_collection self.stdout = self.byte_collection self.stderr = self.byte_collection pass monkeypatch.setattr(helpers, 'Popen', MockPopen) return all_popens

Example tests, must start with the prefix test_ in the test_docker.py file: def test_docker_install(): p = Popen(['which', 'docker'], stdout=PIPE, stderr=PIPE) result = p.stdout.read() assert 'bin/docker' in result def test_copy_file_to_docker(all_popens): result = copy_file_to_docker('asdf', 'asdf') collected_popen = all_popens.pop() mock_read, mock_write, mock_close = collected_popen.byte_collection.get_all_mock_bytes() assert mock_read assert result.args == ['docker', 'cp', 'asdf', 'something_cont:asdf']

def test_docker_exec_something(all_popens): docker_exec_something(something_file_string) collected_popen = all_popens.pop() mock_read, mock_write, mock_close = collected_popen.byte_collection.get_all_mock_bytes() assert len(mock_read) == 3 something_template_stdin = mock_write[0][1][0] these = [os.environ['USER'], os.environ['password_prod'], 'table_name_here', 'test_vdm', 'col_a', 'col_b', '/tmp/test.tsv'] assert all([x in something_template_stdin for x in these])

running the tests one at a time:

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py.test -k test_docker_install tests py.test -k test_copy_file_to_docker tests py.test -k test_docker_exec_something tests

running all the tests in the tests folder: py.test -k test_ tests

Section 192.6: Mocking functions with unittest.mock.create_autospec One way to mock a function is to use the create_autospec function, which will mock out an object according to its specs. With functions, we can use this to ensure that they are called appropriately. With a function multiply in custom_math.py: def multiply(a, b): return a * b

And a function multiples_of in process_math.py: from custom_math import multiply

def multiples_of(integer, *args, num_multiples=0, **kwargs): """ :rtype: list """ multiples = [] for x in range(1, num_multiples + 1): """ Passing in args and kwargs here will only raise TypeError if values were passed to multiples_of function, otherwise they are ignored. This way we can test that multiples_of is used correctly. This is here for an illustration of how create_autospec works. Not recommended for production code. """ multiple = multiply(integer,x, *args, **kwargs) multiples.append(multiple) return multiples

We can test multiples_of alone by mocking out multiply. The below example uses the Python standard library unittest, but this can be used with other testing frameworks as well, like pytest or nose: from unittest.mock import create_autospec import unittest # we import the entire module so we can mock out multiply import custom_math custom_math.multiply = create_autospec(custom_math.multiply) from process_math import multiples_of

class TestCustomMath(unittest.TestCase): def test_multiples_of(self): multiples = multiples_of(3, num_multiples=1) custom_math.multiply.assert_called_with(3, 1)

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def test_multiples_of_with_bad_inputs(self): with self.assertRaises(TypeError) as e: multiples_of(1, "extra arg", num_multiples=1) # this should raise a TypeError

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Chapter 193: py.test Section 193.1: Setting up py.test py.test is one of several third party testing libraries that are available for Python. It can be installed using pip with pip install pytest

The Code to Test Say we are testing an addition function in projectroot/module/code.py: # projectroot/module/code.py def add(a, b): return a + b

The Testing Code We create a test file in projectroot/tests/test_code.py. The file must begin with test_ to be recognized as a testing file. # projectroot/tests/test_code.py from module import code

def test_add(): assert code.add(1, 2) == 3

Running The Test From projectroot we simply run py.test: # ensure we have the modules $ touch tests/__init__.py $ touch module/__init__.py $ py.test ================================================== test session starts =================================================== platform darwin -- Python 2.7.10, pytest-2.9.2, py-1.4.31, pluggy-0.3.1 rootdir: /projectroot, inifile: collected 1 items tests/test_code.py . ================================================ 1 passed in 0.01 seconds ================================================

Section 193.2: Intro to Test Fixtures More complicated tests sometimes need to have things set up before you run the code you want to test. It is possible to do this in the test function itself, but then you end up with large test functions doing so much that it is difficult to tell where the setup stops and the test begins. You can also get a lot of duplicate setup code between your various test functions.

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Our code file: # projectroot/module/stuff.py class Stuff(object): def prep(self): self.foo = 1 self.bar = 2

Our test file: # projectroot/tests/test_stuff.py import pytest from module import stuff

def test_foo_updates(): my_stuff = stuff.Stuff() my_stuff.prep() assert 1 == my_stuff.foo my_stuff.foo = 30000 assert my_stuff.foo == 30000

def test_bar_updates(): my_stuff = stuff.Stuff() my_stuff.prep() assert 2 == my_stuff.bar my_stuff.bar = 42 assert 42 == my_stuff.bar

These are pretty simple examples, but if our Stuff object needed a lot more setup, it would get unwieldy. We see that there is some duplicated code between our test cases, so let's refactor that into a separate function first. # projectroot/tests/test_stuff.py import pytest from module import stuff

def get_prepped_stuff(): my_stuff = stuff.Stuff() my_stuff.prep() return my_stuff

def test_foo_updates(): my_stuff = get_prepped_stuff() assert 1 == my_stuff.foo my_stuff.foo = 30000 assert my_stuff.foo == 30000

def test_bar_updates(): my_stuff = get_prepped_stuff() assert 2 == my_stuff.bar my_stuff.bar = 42 assert 42 == my_stuff.bar

This looks better but we still have the my_stuff = get_prepped_stuff() call cluttering up our test functions. py.test fixtures to the rescue! GoalKicker.com – Python® Notes for Professionals

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Fixtures are much more powerful and flexible versions of test setup functions. They can do a lot more than we're leveraging here, but we'll take it one step at a time. First we change get_prepped_stuff to a fixture called prepped_stuff. You want to name your fixtures with nouns rather than verbs because of how the fixtures will end up being used in the test functions themselves later. The @pytest.fixture indicates that this specific function should be handled as a fixture rather than a regular function. @pytest.fixture def prepped_stuff(): my_stuff = stuff.Stuff() my_stuff.prep() return my_stuff

Now we should update the test functions so that they use the fixture. This is done by adding a parameter to their definition that exactly matches the fixture name. When py.test executes, it will run the fixture before running the test, then pass the return value of the fixture into the test function through that parameter. (Note that fixtures don't need to return a value; they can do other setup things instead, like calling an external resource, arranging things on the filesystem, putting values in a database, whatever the tests need for setup) def test_foo_updates(prepped_stuff): my_stuff = prepped_stuff assert 1 == my_stuff.foo my_stuff.foo = 30000 assert my_stuff.foo == 30000

def test_bar_updates(prepped_stuff): my_stuff = prepped_stuff assert 2 == my_stuff.bar my_stuff.bar = 42 assert 42 == my_stuff.bar

Now you can see why we named it with a noun. but the my_stuff = prepped_stuff line is pretty much useless, so let's just use prepped_stuff directly instead. def test_foo_updates(prepped_stuff): assert 1 == prepped_stuff.foo prepped_stuff.foo = 30000 assert prepped_stuff.foo == 30000

def test_bar_updates(prepped_stuff): assert 2 == prepped_stuff.bar prepped_stuff.bar = 42 assert 42 == prepped_stuff.bar

Now we're using fixtures! We can go further by changing the scope of the fixture (so it only runs once per test module or test suite execution session instead of once per test function), building fixtures that use other fixtures, parametrizing the fixture (so that the fixture and all tests using that fixture are run multiple times, once for each parameter given to the fixture), fixtures that read values from the module that calls them... as mentioned earlier, fixtures have a lot more power and flexibility than a normal setup function. Cleaning up after the tests are done. Let's say our code has grown and our Stuff object now needs special clean up. # projectroot/module/stuff.py

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class Stuff(object): def prep(self): self.foo = 1 self.bar = 2 def finish(self): self.foo = 0 self.bar = 0

We could add some code to call the clean up at the bottom of every test function, but fixtures provide a better way to do this. If you add a function to the fixture and register it as a finalizer, the code in the finalizer function will get called after the test using the fixture is done. If the scope of the fixture is larger than a single function (like module or session), the finalizer will be executed after all the tests in scope are completed, so after the module is done running or at the end of the entire test running session. @pytest.fixture def prepped_stuff(request): # we need to pass in the request to use finalizers my_stuff = stuff.Stuff() my_stuff.prep() def fin(): # finalizer function # do all the cleanup here my_stuff.finish() request.addfinalizer(fin) # register fin() as a finalizer # you can do more setup here if you really want to return my_stuff

Using the finalizer function inside a function can be a bit hard to understand at first glance, especially when you have more complicated fixtures. You can instead use a yield fixture to do the same thing with a more human readable execution flow. The only real difference is that instead of using return we use a yield at the part of the fixture where the setup is done and control should go to a test function, then add all the cleanup code after the yield. We also decorate it as a yield_fixture so that py.test knows how to handle it. @pytest.yield_fixture def prepped_stuff(): # it doesn't need request now! # do setup my_stuff = stuff.Stuff() my_stuff.prep() # setup is done, pass control to the test functions yield my_stuff # do cleanup my_stuff.finish()

And that concludes the Intro to Test Fixtures! For more information, see the official py.test fixture documentation and the official yield fixture documentation

Section 193.3: Failing Tests A failing test will provide helpful output as to what went wrong: # projectroot/tests/test_code.py from module import code

def test_add__failing(): assert code.add(10, 11) == 33

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Results: $ py.test ================================================== test session starts =================================================== platform darwin -- Python 2.7.10, pytest-2.9.2, py-1.4.31, pluggy-0.3.1 rootdir: /projectroot, inifile: collected 1 items tests/test_code.py F ======================================================== FAILURES ======================================================== ___________________________________________________ test_add__failing ____________________________________________________

> E E E

def test_add__failing(): assert code.add(10, 11) == 33 assert 21 == 33 + where 21 = (10, 11) + where = code.add

tests/test_code.py:5: AssertionError ================================================ 1 failed in 0.01 seconds ================================================

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Chapter 194: Profiling Section 194.1: %%timeit and %timeit in IPython Profiling string concatenation: In [1]: import string In [2]: %%timeit s=""; long_list=list(string.ascii_letters)*50 ....: for substring in long_list: ....: s+=substring ....: 1000 loops, best of 3: 570 us per loop In [3]: %%timeit long_list=list(string.ascii_letters)*50 ....: s="".join(long_list) ....: 100000 loops, best of 3: 16.1 us per loop

Profiling loops over iterables and lists: In [4]: %timeit for i in range(100000):pass 100 loops, best of 3: 2.82 ms per loop In [5]: %timeit for i in list(range(100000)):pass 100 loops, best of 3: 3.95 ms per loop

Section 194.2: Using cProfile (Preferred Profiler) Python includes a profiler called cProfile. This is generally preferred over using timeit. It breaks down your entire script and for each method in your script it tells you: ncalls: The number of times a method was called tottime: Total time spent in the given function (excluding time made in calls to sub-functions) percall: Time spent per call. Or the quotient of tottime divided by ncalls cumtime: The cumulative time spent in this and all subfunctions (from invocation till exit). This figure is

accurate even for recursive functions. percall: is the quotient of cumtime divided by primitive calls filename:lineno(function): provides the respective data of each function

The cProfiler can be easily called on Command Line using: $ python -m cProfile main.py

To sort the returned list of profiled methods by the time taken in the method: $ python -m cProfile -s time main.py

Section 194.3: timeit() function Profiling repetition of elements in an array >>> import timeit

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>>> timeit.timeit('list(itertools.repeat("a", 100))', 'import itertools', number = 10000000) 10.997665435877963 >>> timeit.timeit('["a"]*100', number = 10000000) 7.118789926862576

Section 194.4: timeit command line Profiling concatenation of numbers python -m timeit "'-'.join(str(n) for n in range(100))" 10000 loops, best of 3: 29.2 usec per loop python -m timeit "'-'.join(map(str,range(100)))" 100000 loops, best of 3: 19.4 usec per loop

Section 194.5: line_profiler in command line The source code with @profile directive before the function we want to profile: import requests @profile def slow_func(): s = requests.session() html=s.get("https://en.wikipedia.org/").text sum([pow(ord(x),3.1) for x in list(html)]) for i in range(50): slow_func()

Using kernprof command to calculate profiling line by line $ kernprof -lv so6.py Wrote profile results to so6.py.lprof Timer unit: 4.27654e-07 s Total time: 22.6427 s File: so6.py Function: slow_func at line 4 Line # Hits Time Per Hit % Time Line Contents ============================================================== 4 @profile 5 def slow_func(): 6 50 20729 414.6 0.0 s = requests.session() 7 50 47618627 952372.5 89.9 html=s.get("https://en.wikipedia.org/").text 8 50 5306958 106139.2 10.0 sum([pow(ord(x),3.1) for x in list(html)])

Page request is almost always slower than any calculation based on the information on the page.

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Chapter 195: Python speed of program Section 195.1: Deque operations A deque is a double-ended queue. class Deque: def __init__(self): self.items = [] def isEmpty(self): return self.items == [] def addFront(self, item): self.items.append(item) def addRear(self, item): self.items.insert(0,item) def removeFront(self): return self.items.pop() def removeRear(self): return self.items.pop(0) def size(self): return len(self.items)

Operations : Average Case (assumes parameters are randomly generated) Append : O(1) Appendleft : O(1) Copy : O(n) Extend : O(k) Extendleft : O(k) Pop : O(1) Popleft : O(1) Remove : O(n) Rotate : O(k)

Section 195.2: Algorithmic Notations There are certain principles that apply to optimization in any computer language, and Python is no exception. Don't optimize as you go: Write your program without regard to possible optimizations, concentrating instead on making sure that the code is clean, correct, and understandable. If it's too big or too slow when you've finished, then you can consider optimizing it. Remember the 80/20 rule: In many fields you can get 80% of the result with 20% of the effort (also called the

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90/10 rule - it depends on who you talk to). Whenever you're about to optimize code, use profiling to find out where that 80% of execution time is going, so you know where to concentrate your effort. Always run "before" and "after" benchmarks: How else will you know that your optimizations actually made a difference? If your optimized code turns out to be only slightly faster or smaller than the original version, undo your changes and go back to the original, clear code. Use the right algorithms and data structures: Don't use an O(n2) bubble sort algorithm to sort a thousand elements when there's an O(n log n) quicksort available. Similarly, don't store a thousand items in an array that requires an O(n) search when you could use an O(log n) binary tree, or an O(1) Python hash table. For more visit the link below... Python Speed Up The following 3 asymptotic notations are mostly used to represent time complexity of algorithms. 1. Θ Notation: The theta notation bounds a functions from above and below, so it defines exact asymptotic behavior. A simple way to get Theta notation of an expression is to drop low order terms and ignore leading constants. For example, consider the following expression. 3n3 + 6n2 + 6000 = Θ(n3) Dropping lower order terms is always fine because there will always be a n0 after which Θ(n3) has higher values than Θn2) irrespective of the constants involved. For a given function g(n), we denote Θ(g(n)) is following set of functions. Θ(g(n)) = {f(n): there exist positive constants c1, c2 and n0 such that 0 b'.\xdf\xda\xdaVR[\x12\x90\xff\x16\xfb\x17D\xcf\xb4\x82\xdd)\x14\xff\xbc\xb6Iy\x0c\x0eX\x9eF-='

Note that you can call update an arbitrary number of times before calling digest which is useful to hash a large file chunk by chunk. You can also get the digest in hexadecimal format by using hexdigest: h.hexdigest() # ==> '2edfdada56525b1290ff16fb1744cfb482dd2914ffbcb649790c0e589e462d3d'

Section 197.3: Available Hashing Algorithms hashlib.new requires the name of an algorithm when you call it to produce a generator. To find out what

algorithms are available in the current Python interpreter, use hashlib.algorithms_available: GoalKicker.com – Python® Notes for Professionals

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import hashlib hashlib.algorithms_available # ==> {'sha256', 'DSA-SHA', 'SHA512', 'SHA224', 'dsaWithSHA', 'SHA', 'RIPEMD160', 'ecdsa-with-SHA1', 'sha1', 'SHA384', 'md5', 'SHA1', 'MD5', 'MD4', 'SHA256', 'sha384', 'md4', 'ripemd160', 'sha224', 'sha512', 'DSA', 'dsaEncryption', 'sha', 'whirlpool'}

The returned list will vary according to platform and interpreter; make sure you check your algorithm is available. There are also some algorithms that are guaranteed to be available on all platforms and interpreters, which are available using hashlib.algorithms_guaranteed: hashlib.algorithms_guaranteed # ==> {'sha256', 'sha384', 'sha1', 'sha224', 'md5', 'sha512'}

Section 197.4: File Hashing A hash is a function that converts a variable length sequence of bytes to a fixed length sequence. Hashing files can be advantageous for many reasons. Hashes can be used to check if two files are identical or verify that the contents of a file haven't been corrupted or changed. You can use hashlib to generate a hash for a file: import hashlib hasher = hashlib.new('sha256') with open('myfile', 'r') as f: contents = f.read() hasher.update(contents) print hasher.hexdigest()

For larger files, a buffer of fixed length can be used: import hashlib SIZE = 65536 hasher = hashlib.new('sha256') with open('myfile', 'r') as f: buffer = f.read(SIZE) while len(buffer) > 0: hasher.update(buffer) buffer = f.read(SIZE) print(hasher.hexdigest())

Section 197.5: Generating RSA signatures using pycrypto RSA can be used to create a message signature. A valid signature can only be generated with access to the private RSA key, validating on the other hand is possible with merely the corresponding public key. So as long as the other side knows your public key they can verify the message to be signed by you and unchanged - an approach used for email for example. Currently, a third-party module like pycrypto is required for this functionality. import errno from Crypto.Hash import SHA256 from Crypto.PublicKey import RSA from Crypto.Signature import PKCS1_v1_5

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message = b'This message is from me, I promise.' try: with open('privkey.pem', 'r') as f: key = RSA.importKey(f.read()) except IOError as e: if e.errno != errno.ENOENT: raise # No private key, generate a new one. This can take a few seconds. key = RSA.generate(4096) with open('privkey.pem', 'wb') as f: f.write(key.exportKey('PEM')) with open('pubkey.pem', 'wb') as f: f.write(key.publickey().exportKey('PEM')) hasher = SHA256.new(message) signer = PKCS1_v1_5.new(key) signature = signer.sign(hasher)

Verifying the signature works similarly but uses the public key rather than the private key: with open('pubkey.pem', 'rb') as f: key = RSA.importKey(f.read()) hasher = SHA256.new(message) verifier = PKCS1_v1_5.new(key) if verifier.verify(hasher, signature): print('Nice, the signature is valid!') else: print('No, the message was signed with the wrong private key or modified')

Note: The above examples use PKCS#1 v1.5 signing algorithm which is very common. pycrypto also implements the newer PKCS#1 PSS algorithm, replacing PKCS1_v1_5 by PKCS1_PSS in the examples should work if you want to use that one. Currently there seems to be little reason to use it however.

Section 197.6: Asymmetric RSA encryption using pycrypto Asymmetric encryption has the advantage that a message can be encrypted without exchanging a secret key with the recipient of the message. The sender merely needs to know the recipients public key, this allows encrypting the message in such a way that only the designated recipient (who has the corresponding private key) can decrypt it. Currently, a third-party module like pycrypto is required for this functionality. from Crypto.Cipher import PKCS1_OAEP from Crypto.PublicKey import RSA message = b'This is a very secret message.' with open('pubkey.pem', 'rb') as f: key = RSA.importKey(f.read()) cipher = PKCS1_OAEP.new(key) encrypted = cipher.encrypt(message)

The recipient can decrypt the message then if they have the right private key: with open('privkey.pem', 'rb') as f: key = RSA.importKey(f.read()) cipher = PKCS1_OAEP.new(key) decrypted = cipher.decrypt(encrypted)

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Note: The above examples use PKCS#1 OAEP encryption scheme. pycrypto also implements PKCS#1 v1.5 encryption scheme, this one is not recommended for new protocols however due to known caveats.

Section 197.7: Symmetric encryption using pycrypto Python's built-in crypto functionality is currently limited to hashing. Encryption requires a third-party module like pycrypto. For example, it provides the AES algorithm which is considered state of the art for symmetric encryption. The following code will encrypt a given message using a passphrase: import hashlib import math import os from Crypto.Cipher import AES IV_SIZE = 16 KEY_SIZE = 32 SALT_SIZE = 16

# 128 bit, fixed for the AES algorithm # 256 bit meaning AES-256, can also be 128 or 192 bits # This size is arbitrary

cleartext = b'Lorem ipsum' password = b'highly secure encryption password' salt = os.urandom(SALT_SIZE) derived = hashlib.pbkdf2_hmac('sha256', password, salt, 100000, dklen=IV_SIZE + KEY_SIZE) iv = derived[0:IV_SIZE] key = derived[IV_SIZE:] encrypted = salt + AES.new(key, AES.MODE_CFB, iv).encrypt(cleartext)

The AES algorithm takes three parameters: encryption key, initialization vector (IV) and the actual message to be encrypted. If you have a randomly generated AES key then you can use that one directly and merely generate a random initialization vector. A passphrase doesn't have the right size however, nor would it be recommendable to use it directly given that it isn't truly random and thus has comparably little entropy. Instead, we use the built-in implementation of the PBKDF2 algorithm to generate a 128 bit initialization vector and 256 bit encryption key from the password. Note the random salt which is important to have a different initialization vector and key for each message encrypted. This ensures in particular that two equal messages won't result in identical encrypted text, but it also prevents attackers from reusing work spent guessing one passphrase on messages encrypted with another passphrase. This salt has to be stored along with the encrypted message in order to derive the same initialization vector and key for decrypting. The following code will decrypt our message again: salt = encrypted[0:SALT_SIZE] derived = hashlib.pbkdf2_hmac('sha256', password, salt, 100000, dklen=IV_SIZE + KEY_SIZE) iv = derived[0:IV_SIZE] key = derived[IV_SIZE:] cleartext = AES.new(key, AES.MODE_CFB, iv).decrypt(encrypted[SALT_SIZE:])

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Chapter 198: Secure Shell Connection in Python Parameter Usage hostname This parameter tells the host to which the connection needs to be established username username required to access the host port

host port

password

password for the account

Section 198.1: ssh connection from paramiko import client ssh = client.SSHClient() # create a new SSHClient object ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy()) #auto-accept unknown host keys ssh.connect(hostname, username=username, port=port, password=password) #connect with a host stdin, stdout, stderr = ssh.exec_command(command) # submit a command to ssh print stdout.channel.recv_exit_status() #tells the status 1 - job failed

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Chapter 199: Python Anti-Patterns Section 199.1: Overzealous except clause Exceptions are powerful, but a single overzealous except clause can take it all away in a single line. try: res = get_result() res = res[0] log('got result: %r' % res) except: if not res: res = '' print('got exception') This example demonstrates 3 symptoms of the antipattern: 1. The except with no exception type (line 5) will catch even healthy exceptions, including KeyboardInterrupt. That will prevent the program from exiting in some cases. 2. The except block does not reraise the error, meaning that we won't be able to tell if the exception came from within get_result or because res was an empty list. 3. Worst of all, if we were worried about result being empty, we've caused something much worse. If get_result fails, res will stay completely unset, and the reference to res in the except block, will raise NameError, completely masking the original error.

Always think about the type of exception you're trying to handle. Give the exceptions page a read and get a feel for what basic exceptions exist. Here is a fixed version of the example above: import traceback try: res = get_result() except Exception: log_exception(traceback.format_exc()) raise try: res = res[0] except IndexError: res = '' log('got result: %r' % res) We catch more specific exceptions, reraising where necessary. A few more lines, but infinitely more correct.

Section 199.2: Looking before you leap with processorintensive function A program can easily waste time by calling a processor-intensive function multiple times. For example, take a function which looks like this: it returns an integer if the input value can produce one, else None: def intensive_f(value): # int -> Optional[int] # complex, and time-consuming code if process_has_failed: return None return integer_output

And it could be used in the following way: x = 5 if intensive_f(x) is not None: print(intensive_f(x) / 2) else: print(x, "could not be processed") print(x)

Whilst this will work, it has the problem of calling intensive_f, which doubles the length of time for the code to run. A better solution would be to get the return value of the function beforehand. GoalKicker.com – Python® Notes for Professionals

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x = 5 result = intensive_f(x) if result is not None: print(result / 2) else: print(x, "could not be processed")

However, a clearer and possibly more pythonic way is to use exceptions, for example: x = 5 try: print(intensive_f(x) / 2) except TypeError: # The exception raised if None + 1 is attempted print(x, "could not be processed")

Here no temporary variable is needed. It may often be preferable to use a assert statement, and to catch the AssertionError instead.

Dictionary keys A common example of where this may be found is accessing dictionary keys. For example compare: bird_speeds = get_very_long_dictionary() if "european swallow" in bird_speeds: speed = bird_speeds["european swallow"] else: speed = input("What is the air-speed velocity of an unladen swallow?") print(speed)

with: bird_speeds = get_very_long_dictionary() try: speed = bird_speeds["european swallow"] except KeyError: speed = input("What is the air-speed velocity of an unladen swallow?") print(speed)

The first example has to look through the dictionary twice, and as this is a long dictionary, it may take a long time to do so each time. The second only requires one search through the dictionary, and thus saves a lot of processor time. An alternative to this is to use dict.get(key, default), however many circumstances may require more complex operations to be done in the case that the key is not present

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Chapter 200: Common Pitfalls Python is a language meant to be clear and readable without any ambiguities and unexpected behaviors. Unfortunately, these goals are not achievable in all cases, and that is why Python does have a few corner cases where it might do something different than what you were expecting. This section will show you some issues that you might encounter when writing Python code.

Section 200.1: List multiplication and common references Consider the case of creating a nested list structure by multiplying: li = [[]] * 3 print(li) # Out: [[], [], []]

At first glance we would think we have a list of containing 3 different nested lists. Let's try to append 1 to the first one: li[0].append(1) print(li) # Out: [[1], [1], [1]] 1 got appended to all of the lists in li.

The reason is that [[]] * 3 doesn't create a list of 3 different lists. Rather, it creates a list holding 3 references to the same list object. As such, when we append to li[0] the change is visible in all sub-elements of li. This is equivalent of: li = [] element = [[]] li = element + element + element print(li) # Out: [[], [], []] element.append(1) print(li) # Out: [[1], [1], [1]]

This can be further corroborated if we print the memory addresses of the contained list by using id: li = [[]] * 3 print([id(inner_list) for inner_list in li]) # Out: [6830760, 6830760, 6830760]

The solution is to create the inner lists with a loop: li = [[] for _ in range(3)]

Instead of creating a single list and then making 3 references to it, we now create 3 different distinct lists. This, again, can be verified by using the id function: print([id(inner_list) for inner_list in li]) # Out: [6331048, 6331528, 6331488]

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You can also do this. It causes a new empty list to be created in each append call. >>> li = [] >>> li.append([]) >>> li.append([]) >>> li.append([]) >>> for k in li: print(id(k)) ... 4315469256 4315564552 4315564808

Don't use index to loop over a sequence. Don't: for i in range(len(tab)): print(tab[i])

Do: for elem in tab: print(elem) for will automate most iteration operations for you.

Use enumerate if you really need both the index and the element. for i, elem in enumerate(tab): print((i, elem))

Be careful when using "==" to check against True or False if (var == True): # this will execute if var is True or 1, 1.0, 1L if (var != True): # this will execute if var is neither True nor 1 if (var == False): # this will execute if var is False or 0 (or 0.0, 0L, 0j) if (var == None): # only execute if var is None if var: # execute if var is a non-empty string/list/dictionary/tuple, non-0, etc if not var: # execute if var is "", {}, [], (), 0, None, etc. if var is True: # only execute if var is boolean True, not 1 if var is False: # only execute if var is boolean False, not 0 if var is None:

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# same as var == None

Do not check if you can, just do it and handle the error Pythonistas usually say "It's easier to ask for forgiveness than permission". Don't: if os.path.isfile(file_path): file = open(file_path) else: # do something

Do: try: file = open(file_path) except OSError as e: # do something

Or even better with Python 2.6+: with open(file_path) as file:

It is much better because it is much more generic. You can apply try/except to almost anything. You don't need to care about what to do to prevent it, just care about the error you are risking. Do not check against type Python is dynamically typed, therefore checking for type makes you lose flexibility. Instead, use duck typing by checking behavior. If you expect a string in a function, then use str() to convert any object to a string. If you expect a list, use list() to convert any iterable to a list. Don't: def foo(name): if isinstance(name, str): print(name.lower()) def bar(listing): if isinstance(listing, list): listing.extend((1, 2, 3)) return ", ".join(listing)

Do: def foo(name) : print(str(name).lower()) def bar(listing) : l = list(listing) l.extend((1, 2, 3)) return ", ".join(l)

Using the last way, foo will accept any object. bar will accept strings, tuples, sets, lists and much more. Cheap DRY. Don't mix spaces and tabs GoalKicker.com – Python® Notes for Professionals

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Use object as first parent This is tricky, but it will bite you as your program grows. There are old and new classes in Python 2.x. The old ones are, well, old. They lack some features, and can have awkward behavior with inheritance. To be usable, any of your class must be of the "new style". To do so, make it inherit from object. Don't: class Father: pass class Child(Father): pass

Do: class Father(object): pass

class Child(Father): pass

In Python 3.x all classes are new style so you don't need to do that. Don't initialize class attributes outside the init method People coming from other languages find it tempting because that is what you do in Java or PHP. You write the class name, then list your attributes and give them a default value. It seems to work in Python, however, this doesn't work the way you think. Doing that will setup class attributes (static attributes), then when you will try to get the object attribute, it will gives you its value unless it's empty. In that case it will return the class attributes. It implies two big hazards: If the class attribute is changed, then the initial value is changed. If you set a mutable object as a default value, you'll get the same object shared across instances. Don't (unless you want static): class Car(object): color = "red" wheels = [Wheel(), Wheel(), Wheel(), Wheel()]

Do: class Car(object): def __init__(self): self.color = "red" self.wheels = [Wheel(), Wheel(), Wheel(), Wheel()]

Section 200.2: Mutable default argument def foo(li=[]): li.append(1) print(li)

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foo([2]) # Out: [2, 1] foo([3]) # Out: [3, 1]

This code behaves as expected, but what if we don't pass an argument? foo() # Out: [1] As expected... foo() # Out: [1, 1]

Not as expected...

This is because default arguments of functions and methods are evaluated at definition time rather than run time. So we only ever have a single instance of the li list. The way to get around it is to use only immutable types for default arguments: def foo(li=None): if not li: li = [] li.append(1) print(li) foo() # Out: [1] foo() # Out: [1]

While an improvement and although if not li correctly evaluates to False, many other objects do as well, such as zero-length sequences. The following example arguments can cause unintended results: x = [] foo(li=x) # Out: [1] foo(li="") # Out: [1] foo(li=0) # Out: [1]

The idiomatic approach is to directly check the argument against the None object: def foo(li=None): if li is None: li = [] li.append(1) print(li) foo() # Out: [1]

Section 200.3: Changing the sequence you are iterating over A for loop iterates over a sequence, so altering this sequence inside the loop could lead to unexpected results (especially when adding or removing elements): GoalKicker.com – Python® Notes for Professionals

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alist = [0, 1, 2] for index, value in enumerate(alist): alist.pop(index) print(alist) # Out: [1]

Note: list.pop() is being used to remove elements from the list. The second element was not deleted because the iteration goes through the indices in order. The above loop iterates twice, with the following results: # Iteration #1 index = 0 alist = [0, 1, 2] alist.pop(0) # removes '0' # Iteration #2 index = 1 alist = [1, 2] alist.pop(1) # removes '2' # loop terminates, but alist is not empty: alist = [1]

This problem arises because the indices are changing while iterating in the direction of increasing index. To avoid this problem, you can iterate through the loop backwards: alist = [1,2,3,4,5,6,7] for index, item in reversed(list(enumerate(alist))): # delete all even items if item % 2 == 0: alist.pop(index) print(alist) # Out: [1, 3, 5, 7]

By iterating through the loop starting at the end, as items are removed (or added), it does not affect the indices of items earlier in the list. So this example will properly remove all items that are even from alist. A similar problem arises when inserting or appending elements to a list that you are iterating over, which can result in an infinite loop: alist = [0, 1, 2] for index, value in enumerate(alist): # break to avoid infinite loop: if index == 20: break alist.insert(index, 'a') print(alist) # Out (abbreviated): ['a', 'a', ..., 'a', 'a',

0,

1,

2]

Without the break condition the loop would insert 'a' as long as the computer does not run out of memory and the program is allowed to continue. In a situation like this, it is usually preferred to create a new list, and add items to the new list as you loop through the original list. When using a for loop, you cannot modify the list elements with the placeholder variable: alist = [1,2,3,4]

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for item in alist: if item % 2 == 0: item = 'even' print(alist) # Out: [1,2,3,4]

In the above example, changing item doesn't actually change anything in the original list. You need to use the list index (alist[2]), and enumerate() works well for this: alist = [1,2,3,4] for index, item in enumerate(alist): if item % 2 == 0: alist[index] = 'even' print(alist) # Out: [1, 'even', 3, 'even']

A while loop might be a better choice in some cases: If you are going to delete all the items in the list: zlist = [0, 1, 2] while zlist: print(zlist[0]) zlist.pop(0) print('After: zlist =', zlist) # Out: 0 # 1 # 2 # After: zlist = []

Although simply resetting zlist will accomplish the same result; zlist = []

The above example can also be combined with len() to stop after a certain point, or to delete all but x items in the list: zlist = [0, 1, 2] x = 1 while len(zlist) > x: print(zlist[0]) zlist.pop(0) print('After: zlist =', zlist) # Out: 0 # 1 # After: zlist = [2]

Or to loop through a list while deleting elements that meet a certain condition (in this case deleting all even elements): zlist = [1,2,3,4,5] i = 0 while i < len(zlist): if zlist[i] % 2 == 0: zlist.pop(i) else:

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i += 1 print(zlist) # Out: [1, 3, 5]

Notice that you don't increment i after deleting an element. By deleting the element at zlist[i], the index of the next item has decreased by one, so by checking zlist[i] with the same value for i on the next iteration, you will be correctly checking the next item in the list. A contrary way to think about removing unwanted items from a list, is to add wanted items to a new list. The following example is an alternative to the latter while loop example: zlist = [1,2,3,4,5] z_temp = [] for item in zlist: if item % 2 != 0: z_temp.append(item) zlist = z_temp print(zlist) # Out: [1, 3, 5]

Here we are funneling desired results into a new list. We can then optionally reassign the temporary list to the original variable. With this trend of thinking, you can invoke one of Python's most elegant and powerful features, list comprehensions, which eliminates temporary lists and diverges from the previously discussed in-place list/index mutation ideology. zlist = [1,2,3,4,5] [item for item in zlist if item % 2 != 0] # Out: [1, 3, 5]

Section 200.4: Integer and String identity Python uses internal caching for a range of integers to reduce unnecessary overhead from their repeated creation. In effect, this can lead to confusing behavior when comparing integer identities: >>> -8 is (-7 - 1) False >>> -3 is (-2 - 1) True

and, using another example: >>> (255 + 1) is (255 + 1) True >>> (256 + 1) is (256 + 1) False

Wait what? We can see that the identity operation is yields True for some integers (-3, 256) but no for others (-8, 257). To be more specific, integers in the range [-5, 256] are internally cached during interpreter startup and are only created once. As such, they are identical and comparing their identities with is yields True; integers outside this GoalKicker.com – Python® Notes for Professionals

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range are (usually) created on-the-fly and their identities compare to False. This is a common pitfall since this is a common range for testing, but often enough, the code fails in the later staging process (or worse - production) with no apparent reason after working perfectly in development. The solution is to always compare values using the equality (==) operator and not the identity (is) operator. Python also keeps references to commonly used strings and can result in similarly confusing behavior when comparing identities (i.e. using is) of strings. >>> 'python' is 'py' + 'thon' True

The string 'python' is commonly used, so Python has one object that all references to the string 'python' use. For uncommon strings, comparing identity fails even when the strings are equal. >>> 'this is not a common string' is 'this is not' + ' a common string' False >>> 'this is not a common string' == 'this is not' + ' a common string' True

So, just like the rule for Integers, always compare string values using the equality (==) operator and not the identity (is) operator.

Section 200.5: Dictionaries are unordered You might expect a Python dictionary to be sorted by keys like, for example, a C++ std::map, but this is not the case: myDict = {'first': 1, 'second': 2, 'third': 3} print(myDict) # Out: {'first': 1, 'second': 2, 'third': 3} print([k for k in myDict]) # Out: ['second', 'third', 'first']

Python doesn't have any built-in class that automatically sorts its elements by key. However, if sorting is not a must, and you just want your dictionary to remember the order of insertion of its key/value pairs, you can use collections.OrderedDict: from collections import OrderedDict oDict = OrderedDict([('first', 1), ('second', 2), ('third', 3)]) print([k for k in oDict]) # Out: ['first', 'second', 'third']

Keep in mind that initializing an OrderedDict with a standard dictionary won't sort in any way the dictionary for you. All that this structure does is to preserve the order of key insertion. The implementation of dictionaries was changed in Python 3.6 to improve their memory consumption. A side effect of this new implementation is that it also preserves the order of keyword arguments passed to a function: Python 3.x Version

≥ 3.6

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def func(**kw): print(kw.keys()) func(a=1, b=2, c=3, d=4, e=5) dict_keys(['a', 'b', 'c', 'd', 'e']) # expected order

Caveat: beware that “the order-preserving aspect of this new implementation is considered an implementation detail and should not be relied upon”, as it may change in the future.

Section 200.6: Variable leaking in list comprehensions and for loops Consider the following list comprehension Python 2.x Version

≤ 2.7

i = 0 a = [i for i in range(3)] print(i) # Outputs 2

This occurs only in Python 2 due to the fact that the list comprehension “leaks” the loop control variable into the surrounding scope (source). This behavior can lead to hard-to-find bugs and it has been fixed in Python 3. Python 3.x Version

≥ 3.0

i = 0 a = [i for i in range(3)] print(i) # Outputs 0

Similarly, for loops have no private scope for their iteration variable i = 0 for i in range(3): pass print(i) # Outputs 2

This type of behavior occurs both in Python 2 and Python 3. To avoid issues with leaking variables, use new variables in list comprehensions and for loops as appropriate.

Section 200.7: Chaining of or operator When testing for any of several equality comparisons: if a == 3 or b == 3 or c == 3:

it is tempting to abbreviate this to if a or b or c == 3: # Wrong

This is wrong; the or operator has lower precedence than ==, so the expression will be evaluated as if (a) or (b) or (c == 3):. The correct way is explicitly checking all the conditions: if a == 3 or b == 3 or c == 3:

# Right Way

Alternately, the built-in any() function may be used in place of chained or operators: GoalKicker.com – Python® Notes for Professionals

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if any([a == 3, b == 3, c == 3]): # Right

Or, to make it more efficient: if any(x == 3 for x in (a, b, c)): # Right

Or, to make it shorter: if 3 in (a, b, c): # Right

Here, we use the in operator to test if the value is present in a tuple containing the values we want to compare against. Similarly, it is incorrect to write if a == 1 or 2 or 3:

which should be written as if a in (1, 2, 3):

Section 200.8: sys.argv[0] is the name of the file being executed The first element of sys.argv[0] is the name of the python file being executed. The remaining elements are the script arguments. # script.py import sys print(sys.argv[0]) print(sys.argv)

$ python script.py => script.py => ['script.py'] $ python script.py fizz => script.py => ['script.py', 'fizz'] $ python script.py fizz buzz => script.py => ['script.py', 'fizz', 'buzz']

Section 200.9: Accessing int literals' attributes You might have heard that everything in Python is an object, even literals. This means, for example, 7 is an object as well, which means it has attributes. For example, one of these attributes is the bit_length. It returns the amount of bits needed to represent the value it is called upon. x = 7 x.bit_length() # Out: 3

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Seeing the above code works, you might intuitively think that 7.bit_length() would work as well, only to find out it raises a SyntaxError. Why? because the interpreter needs to differentiate between an attribute access and a floating number (for example 7.2 or 7.bit_length()). It can't, and that's why an exception is raised. There are a few ways to access an int literals' attributes: # parenthesis (7).bit_length() # a space 7 .bit_length()

Using two dots (like this 7..bit_length()) doesn't work in this case, because that creates a float literal and floats don't have the bit_length() method. This problem doesn't exist when accessing float literals' attributes since the interpreter is "smart" enough to know that a float literal can't contain two ., for example: 7.2.as_integer_ratio() # Out: (8106479329266893, 1125899906842624)

Section 200.10: Global Interpreter Lock (GIL) and blocking threads Plenty has been written about Python's GIL. It can sometimes cause confusion when dealing with multi-threaded (not to be confused with multiprocess) applications. Here's an example: import math from threading import Thread def calc_fact(num): math.factorial(num) num = 600000 t = Thread(target=calc_fact, daemon=True, args=[num]) print("About to calculate: {}!".format(num)) t.start() print("Calculating...") t.join() print("Calculated")

You would expect to see Calculating... printed out immediately after the thread is started, we wanted the calculation to happen in a new thread after all! But in actuality, you see it get printed after the calculation is complete. That is because the new thread relies on a C function (math.factorial) which will lock the GIL while it executes. There are a couple ways around this. The first is to implement your factorial function in native Python. This will allow the main thread to grab control while you are inside your loop. The downside is that this solution will be a lot slower, since we're not using the C function anymore. def calc_fact(num): """ A slow version of factorial in native Python """ res = 1 while num >= 1: res = res * num

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num -= 1 return res

You can also sleep for a period of time before starting your execution. Note: this won't actually allow your program to interrupt the computation happening inside the C function, but it will allow your main thread to continue after the spawn, which is what you may expect. def calc_fact(num): sleep(0.001) math.factorial(num)

Section 200.11: Multiple return Function xyz returns two values a and b: def xyz(): return a, b

Code calling xyz stores result into one variable assuming xyz returns only one value: t = xyz()

Value of t is actually a tuple (a, b) so any action on t assuming it is not a tuple may fail deep in the code with a an unexpected error about tuples. TypeError: type tuple doesn't define ... method The fix would be to do: a, b = xyz()

Beginners will have trouble finding the reason of this message by only reading the tuple error message !

Section 200.12: Pythonic JSON keys my_var = 'bla'; api_key = 'key'; ...lots of code here... params = {"language": "en", my_var: api_key}

If you are used to JavaScript, variable evaluation in Python dictionaries won't be what you expect it to be. This statement in JavaScript would result in the params object as follows: { "language": "en", "my_var": "key" }

In Python, however, it would result in the following dictionary: { "language": "en", "bla": "key"

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} my_var is evaluated and its value is used as the key.

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Chapter 201: Hidden Features Section 201.1: Operator Overloading Everything in Python is an object. Each object has some special internal methods which it uses to interact with other objects. Generally, these methods follow the __action__ naming convention. Collectively, this is termed as the Python Data Model. You can overload any of these methods. This is commonly used in operator overloading in Python. Below is an example of operator overloading using Python's data model. The Vector class creates a simple vector of two variables. We'll add appropriate support for mathematical operations of two vectors using operator overloading. class Vector(object): def __init__(self, x, y): self.x = x self.y = y def __add__(self, v): # Addition with another vector. return Vector(self.x + v.x, self.y + v.y) def __sub__(self, v): # Subtraction with another vector. return Vector(self.x - v.x, self.y - v.y) def __mul__(self, s): # Multiplication with a scalar. return Vector(self.x * s, self.y * s) def __div__(self, s): # Division with a scalar. float_s = float(s) return Vector(self.x / float_s, self.y / float_s) def __floordiv__(self, s): # Division with a scalar (value floored). return Vector(self.x // s, self.y // s) def __repr__(self): # Print friendly representation of Vector class. Else, it would # show up like, . return '' % (self.x, self.y, ) a = Vector(3, 5) b = Vector(2, 7) print print print print print

a b b a a

+ b # Output: - a # Output: * 1.3 # Output: // 17 # Output: / 17 # Output:

The above example demonstrates overloading of basic numeric operators. A comprehensive list can be found here.

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Credits Thank you greatly to all the people from Stack Overflow Documentation who helped provide this content, more changes can be sent to [email protected] for new content to be published or updated Çağatay Uslu 2Cubed 4444 A. Ciclet A. Raza Aaron Christiansen Aaron Critchley Aaron Hall Abhishek Kumar abukaj acdr Adam Brenecki Adam Matan Adam_92 adeora Aditya Adrian Antunez Adriano afeique Aidan Ajean Akshat Mahajan aldanor Aldo Alec alecxe alejosocorro Alex Gaynor Alex L Alex Logan AlexV Alfe alfonso.kim ALinuxLover Alireza Savand Alleo Alon Alexander amblina Ami Tavory amin Amir Rachum Amitay Stern Anaphory anatoly techtonik Andrea andrew Andrew Schade

Chapter 20 Chapters 39, 122 and 147 Chapter 21 Chapter 196 Chapter 1 Chapters 40 and 109 Chapter 1 Chapter 38 Chapter 149 Chapter 200 Chapter 21 Chapter 85 Chapters 84 and 104 Chapter 40 Chapter 197 Chapters 40, 53, 134, 195 and 200 Chapter 88 Chapter 33 Chapter 1 Chapter 75 Chapter 5 Chapters 33, 67, 120 and 146 Chapter 40 Chapter 103 Chapter 200 Chapters 5, 40, 87, 92 and 93 Chapters 1 and 75 Chapter 55 Chapter 16 Chapter 1 Chapter 33 Chapter 88 Chapter 16 Chapter 1 Chapter 192 Chapters 21 and 149 Chapter 116 Chapters 83, 85 and 129 Chapter 192 Chapter 9 Chapters 19 and 39 Chapters 41 and 91 Chapter 159 Chapter 160 Chapter 1 Chapter 131 Chapter 84

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Andrii Abramov Andrzej Pronobis Andy Andy Hayden angussidney Ani Menon Annonymous Anthony Pham Antoine Bolvy Antoine Pinsard Antti Haapala Antwan APerson Aquib Javed Khan Ares Arkady Arpit Solanki Artem Kolontay ArtOfCode Arun Aryaman Arora ashes999 asmeurer atayenel Athari Avantol13 avb Axe B8vrede Baaing Cow Bahrom Bakuriu balki Barry Bastian bbayles Beall619 bee Benedict Bunting Bharel Bhargav Bhargav Rao bignose Billy Biswa_9937 bitchaser bixel blubberdiblub blueberryfields blueenvelope Bluethon boboquack bogdanciobanu

Chapter 1 Chapters 8, 38 and 55 Chapters 1, 5, 7, 17, 33, 51, 82 and 88 Chapters 8, 33, 67, 73, 75, 77, 98 and 149 Chapter 1 Chapters 1, 4, 40, 149 and 154 Chapters 78, 87 and 199 Chapters 13, 15, 16, 19, 20, 30, 33, 43, 45, 55, 60, 70 and 179 Chapters 1 and 149 Chapter 39 Chapters 5, 16, 72, 87 and 106 Chapter 149 Chapters 21, 69 and 72 Chapter 1 Chapters 1, 15, 20 and 41 Chapter 33 Chapters 1, 82 and 125 Chapter 173 Chapters 67, 173 and 197 Chapters 65 and 108 Chapter 179 Chapter 75 Chapters 45 and 47 Chapter 124 Chapter 151 Chapter 38 Chapter 30 Chapters 21 and 149 Chapters 1, 40, 75 and 103 Chapters 1 and 200 Chapter 8 Chapter 149 Chapter 53 Chapter 20 Chapter 86 Chapter 128 Chapters 74 and 101 Chapters 161 and 164 Chapter 99 Chapters 30 and 149 Chapter 40 Chapters 41, 149 and 200 Chapter 149 Chapter 200 Chapters 178, 182 and 183 Chapter 149 Chapter 200 Chapter 177 Chapter 181 Chapters 9 and 20 Chapter 149 Chapters 10, 11 and 18 Chapter 111

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Bonifacio2 BoppreH Bosoneando Božo Stojković bpachev Brendan Abel brennan Brett Cannon Brian C Brien Bryan P BSL Burhan Khalid BusyAnt Buzz Cache Staheli CamelBackNotation Cameron Gagnon Camsbury caped114 carrdelling Cbeb24404 ceruleus cfi Chandan Purohit ChaoticTwist Charles Charul Chinmay Hegde Chong Tang Chris Hunt Chris Larson Chris Midgley Chris Mueller Christian Ternus Christofer Ohlsson Christophe Roussy Chromium Cilyan Cimbali cizixs cjds Clíodhna Claudiu Clayton Wahlstrom cledoux CodenameLambda Cody Piersall Colin Yang Comrade SparklePony Conrad.Dean crhodes c ʟ s

Chapter 64 Chapter 19 Chapter 46 Chapters 1, 14, 21, 33 and 149 Chapter 53 Chapter 84 Chapter 173 Chapters 11 and 43 Chapter 1 Chapter 41 Chapter 1 Chapters 1 and 197 Chapter 19 Chapters 1, 20, 43, 60, 78 and 88 Chapter 18 Chapter 41 Chapter 33 Chapter 149 Chapters 9 and 33 Chapter 41 Chapter 77 Chapter 1 Chapter 1 Chapters 21, 26 and 69 Chapters 20 and 33 Chapters 21, 33, 41 and 107 Chapters 10, 21, 30, 41, 149 and 200 Chapter 167 Chapters 50, 94, 132, 164 and 171 Chapter 21 Chapter 16 Chapter 33 Chapter 1 Chapter 19 Chapters 1, 16, 43, 51 and 78 Chapter 45 Chapter 200 Chapter 134 Chapter 170 Chapter 8 Chapters 19, 20 and 128 Chapters 110 and 134 Chapter 1 Chapters 1, 31, 67, 75, 80, 83, 88, 111, 118, 153, 192 and 193 Chapter 149 Chapter 108 Chapters 1 and 67 Chapter 8 Chapter 149 Chapters 180 and 181 Chapters 1, 5, 21, 38 and 43 Chapter 30 Chapters 1, 149, 161 and 191

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Dair Daksh Gupta Dania danidee Daniel Daniil Ryzhkov Darkade Darth Kotik Darth Shadow Dartmouth Dave J David David Cullen David Heyman davidism DawnPaladin Dee deeenes deepakkt DeepSpace Delgan denvaar depperm DevD Devesh Saini DhiaTN dhimanta Dilettant Dima Tisnek djaszczurowski Doc dodell Doraemon Doug Henderson Douglas Starnes Dov dreftymac driax Duh Dunatotatos dwanderson eandersson edwinksl eenblam Elazar Eleftheria elegent Ellis Elodin Emma Enamul Hassan engineercoding Enrico Maria De Angelis

Chapters 11, 120 and 173 Chapters 1 and 38 Chapter 1 Chapter 33 Chapter 43 Chapter 173 Chapter 173 Chapter 16 Chapters 1, 40, 75, 97, 149 and 173 Chapters 1, 62, 149 and 150 Chapters 40 and 149 Chapters 9, 33, 124 and 161 Chapters 30 and 121 Chapters 41 and 149 Chapter 13 Chapter 33 Chapter 186 Chapters 1, 20 and 67 Chapter 14 Chapters 9, 16, 77, 100, 149 and 200 Chapters 1, 16, 20 and 40 Chapter 167 Chapters 1, 3, 38, 41 and 99 Chapter 1 Chapter 115 Chapter 16 Chapters 184 and 185 Chapter 200 Chapter 21 Chapter 167 Chapter 143 Chapter 1 Chapter 29 Chapter 41 Chapter 1 Chapter 30 Chapter 40 Chapters 39, 75 and 88 Chapter 149 Chapter 171 Chapter 149 Chapter 127 Chapter 173 Chapter 21 Chapters 1, 7, 8, 13, 15, 16, 20, 21, 28, 38, 41, 67, 87, 111, 144 and 149 Chapter 113 Chapter 33 Chapters 20 and 45 Chapters 33 and 195 Chapters 20 and 21 Chapter 16 Chapter 192 Chapter 1

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enrico.bacis erewok Eric Eric Finn Eric Zhang Erica ericdwang ericmarkmartin Erik Godard EsmaeelE Esteis ettanany Everyone_Else evuez exhuma Fábio Perez Faiz Halde FazeL Felix D. Felk Fermi paradox Fernando Ffisegydd Filip Haglund Firix flamenco Flickerlight Florian Bender FMc Francisco Guimaraes Franck Dernoncourt FrankBr frankyjuang Fred Barclay Freddy fredley freidrichen Frustrated Gal Dreiman ganesh gadila Ganesh K Gareth Latty garg10may Gavin Geeklhem Generic Snake geoffspear Gerard Roche gerrit ghostarbeiter Giannis Spiliopoulos GiantsLoveDeathMetal girish946

Chapters 21, 56 and 149 Chapter 149 Chapter 107 Chapter 16 Chapter 110 Chapter 1 Chapter 149 Chapters 67, 98, 110 and 149 Chapter 1 Chapter 1 Chapters 13 and 21 Chapters 27 and 149 Chapter 149 Chapters 7, 8, 14, 20, 40, 113 and 149 Chapter 20 Chapter 31 Chapters 21, 114 and 119 Chapters 111 and 148 Chapter 16 Chapter 21 Chapter 21 Chapter 173 Chapters 14, 38, 98, 108 and 193 Chapter 1 Chapters 1 and 150 Chapter 56 Chapter 20 Chapter 21 Chapter 43 Chapter 94 Chapter 1 Chapter 36 Chapter 181 Chapters 1 and 157 Chapter 1 Chapter 45 Chapter 21 Chapter 74 Chapters 16, 20, 21, 33, 40, 136 and 137 Chapters 20 and 41 Chapter 148 Chapter 19 Chapters 21 and 149 Chapter 149 Chapters 14, 110 and 124 Chapter 16 Chapter 149 Chapter 1 Chapter 40 Chapters 16, 21, 33, 41, 45, 88, 132 and 149 Chapter 40 Chapters 40 and 164 Chapters 31 and 154

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giucal GoatsWearHats goodmami Greg greut Guy H. Pauwelyn hackvan Hannele Hannes Karppila Harrison hashcode55 ha_1694 heyhey2k hiro protagonist HoverHell Hriddhi Dey Hurkyl hxysayhi Ian IanAuld iankit iBelieve idjaw ifma Igor Raush Ilia Barahovski ilse2005 Inbar Rose Indradhanush Gupta Infinity InitializeSahib intboolstring iScrE4m JF Jörn Hees J0HN j3485 jackskis Jacques de Hooge JakeD James James Elderfield James Taylor JamesS Jan jani japborst Jean jedwards Jeff Hutchins Jeffrey Lin JelmerS

Chapter 55 Chapters 1, 16, 29, 41 and 72 Chapter 75 Chapter 22 Chapter 37 Chapters 16, 19 and 156 Chapter 1 Chapter 164 Chapter 21 Chapters 14 and 134 Chapter 40 Chapter 76 Chapter 173 Chapter 94 Chapters 54 and 200 Chapter 12 Chapter 105 Chapters 21 and 33 Chapters 66 and 168 Chapter 1 Chapters 1 and 21 Chapters 21 and 37 Chapter 19 Chapter 41 Chapter 108 Chapters 1, 19, 20, 38, 39, 45, 67 and 98 Chapters 32, 41 and 88 Chapter 30 Chapter 16 Chapter 49 Chapters 19 and 115 Chapters 38, 39 and 82 Chapters 10, 21 and 45 Chapter 149 Chapters 1, 9, 15, 20, 29, 33, 38, 88, 97, 111, 119, 125 and 149 Chapter 137 Chapters 21 and 67 Chapter 20 Chapters 53 and 67 Chapters 97 and 151 Chapter 22 Chapters 8, 14, 19, 20, 33 and 100 Chapters 20, 40, 45 and 149 Chapter 1 Chapter 21 Chapter 75 Chapter 20 Chapter 86 Chapters 1 and 40 Chapters 1, 102 and 149 Chapter 110 Chapters 1, 16, 75, 132 and 200 Chapter 102

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JGreenwell JHS Jim Fasarakis Hilliard jim opleydulven Jimmy Song jimsug jkdev jkitchen JL Peyret jlarsch jmunsch JNat joel3000 Johan Lundberg John Slegers John Zwinck Jonatan jonrsharpe Joseph True Josh Jossie Calderon jrast JRodDynamite jtbandes Juan T JuanPablo Julien Spronck Julij Jegorov Justin Justin Chadwell Justin M. Ucar j__ Kabie Kallz Kamran Mackey Karl Knechtel KartikKannapur kdopen keiv.fly Kevin Brown KeyWeeUsr KIDJourney Kinifwyne Kiran Vemuri kisanme knight kollery kon psych krato Kunal Marwaha Kwarrtz L3viathan Lafexlos

Chapters 3, 9, 33 and 37 Chapter 21 Chapters 1, 16, 33, 41, 55, 87, 110, 149 and 200 Chapter 1 Chapter 149 Chapters 1 and 20 Chapters 20 and 38 Chapter 33 Chapters 40 and 51 Chapter 38 Chapters 1, 47, 92, 125, 181 and 192 Chapters 10, 20 and 29 Chapters 21, 103 and 192 Chapter 1 Chapters 1 and 149 Chapter 11 Chapters 40, 64 and 87 Chapters 1, 75, 78 and 98 Chapter 1 Chapter 149 Chapter 86 Chapter 16 Chapters 1, 21 and 40 Chapter 70 Chapters 1, 67, 90 and 149 Chapter 110 Chapters 53 and 75 Chapter 188 Chapters 30, 33, 40, 74 and 149 Chapter 125 Chapter 149 Chapter 41 Chapter 149 Chapter 38 Chapter 1 Chapters 67, 69 and 149 Chapters 20 and 38 Chapters 19, 21 and 110 Chapter 194 Chapters 1, 5, 14, 30, 53, 75, 146, 149 and 192 Chapters 80 and 153 Chapter 21 Chapters 43 and 193 Chapter 1 Chapter 1 Chapter 40 Chapter 156 Chapter 47 Chapter 83 Chapter 149 Chapter 21 Chapters 33 and 98 Chapters 1, 9 and 20

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LDP Lee Netherton Leo Leo Thumma Leon Leon Z. Liteye loading... Locane lorenzofeliz LostAvatar Luca Van Oort Luke Taylor lukewrites Lyndsy Simon machine yearning magu_ Mahdi Mahmoud Hashemi Majid Malt manu MANU Marco Pashkov Mario Corchero Mark Mark Miller Mark Omo Markus Meskanen MarkyPython Marlon Abeykoon Martijn Pieters Martin Valgur Math Mathias711 matsjoyce Matt Dodge Matt Giltaji Matt Rowland MattCorr Mattew Whitt mattgathu Matthew max Max Feng mbrig mbsingh Md Sifatul Islam mdegis Mechanic mertyildiran MervS metmirr

Chapter 20 Chapters 21, 33 and 114 Chapters 49 and 97 Chapter 20 Chapter 1 Chapter 74 Chapters 21 and 38 Chapters 83 and 114 Chapter 21 Chapter 2 Chapters 1 and 161 Chapter 165 Chapter 70 Chapter 20 Chapter 21 Chapters 19, 53 and 67 Chapter 77 Chapters 21, 82 and 120 Chapter 199 Chapters 19, 28, 77, 82, 98, 112 and 173 Chapter 200 Chapter 1 Chapter 1 Chapters 29, 39, 40, 83 and 173 Chapter 192 Chapter 125 Chapter 130 Chapter 168 Chapter 21 Chapter 41 Chapter 79 Chapters 1, 67, 77 and 97 Chapter 104 Chapter 16 Chapter 1 Chapters 1 and 9 Chapters 149 and 200 Chapters 33, 40, 173 and 193 Chapter 149 Chapters 4 and 121 Chapters 21, 37, 39, 110, 146, 173 and 192 Chapters 19, 117, 124 and 190 Chapter 77 Chapter 67 Chapter 14 Chapter 21 Chapter 161 Chapters 28 and 75 Chapter 1 Chapters 1, 9, 19 and 28 Chapter 1 Chapter 125 Chapter 162

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Chapter 28 Chapter 192 Chapter 149 Chapter 161 Chapters 1 and 13 Chapter 1 Chapter 1 Chapter 21 Chapters 1 and 149 Chapter 123 Chapter 1 Chapter 20 Chapter 92 Chapter 1 Chapters 9, 16, 19, 21, 26, 33, 37, 41, 43, 45, 48, 55, 64, 67, 68, 69, 70, 71, 72, 73 and 200 Chapters 1, 20, 21, 33, 37, 57, 111 and 149 Chapter 37 Chapter 1 Chapter 33 Chapter 40 Chapter 150 Chapters 40, 75 and 116 Chapters 42 and 89 Chapter 190 Chapters 1, 29 and 41 Chapter 200 Chapter 33 Chapter 173 Chapter 10 Chapters 1 and 92 Chapter 139 Chapter 29 Chapter 5 Chapter 149 Chapters 1, 43, 45 and 149 Chapters 5, 19, 28, 30, 53, 67, 117, 120 and 121 Chapter 40 Chapters 1, 19, 21, 28, 88 and 149 Chapter 173 Chapters 1 and 9 Chapter 54 Chapter 5 Chapter 38 Chapter 200 Chapters 8, 21, 75, 112 and 189 Chapter 149 Chapter 173 Chapters 1, 25, 41, 125, 133 and 134 Chapter 20 Chapter 30 Chapter 21 Chapters 23 and 69

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Pasha Patrick Haugh Paul Paul Weaver paulmorriss Paulo Freitas Paulo Scardine Pavan Nath pcurry Peter Brittain Peter Mølgaard Pallesen Peter Shinners petrs philngo Pigman168 pistache pktangyue poke PolyGeo poppie ppperry Prem Narain Preston proprefenetre proprius PSN pylang PYPL Pythonista pzp Quill qwertyuip9 R Colmenares R Nar Rápli András Régis B. Raghav Rahul Nair RahulHP rajah9 Ram Grandhi RandomHash rassar ravigadila Razik Rednivrug regnarg Reut Sharabani rfkortekaas Ricardo Riccardo Petraglia Richard Fitzhugh rlee827

Chapters 3, 20, 21, 33, 37, 38, 67, 70, 77, 83 and 149 Chapters 1 and 200 Chapter 5 Chapter 88 Chapter 5 Chapter 149 Chapter 39 Chapters 1, 20 and 179 Chapters 29, 110 and 149 Chapter 77 Chapter 73 Chapter 109 Chapter 77 Chapter 16 Chapter 96 Chapter 38 Chapter 149 Chapter 20 Chapter 145 Chapter 149 Chapter 55 Chapter 59 Chapters 138 and 173 Chapter 147 Chapter 5 Chapter 1 Chapters 1, 8, 21, 33, 38, 43, 53, 54, 73, 80, 128, 147, 173, 192 and 200 Chapter 79 Chapters 32 and 149 Chapters 1, 29, 30, 33, 41, 49, 64 and 67 Chapter 1 Chapters 161, 173 and 181 Chapter 10 Chapter 21 Chapter 82 Chapter 173 Chapter 125 Chapters 1, 21, 43, 88 and 143 Chapters 5, 8, 45, 53, 64 and 112 Chapters 14, 16 and 45 Chapter 1 Chapter 95 Chapter 172 Chapters 20, 60, 85, 91 and 104 Chapter 158 Chapters 2, 35 and 63 Chapter 75 Chapters 64 and 200 Chapters 1 and 115 Chapter 157 Chapters 21, 84 and 117 Chapter 38 Chapter 62

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rll Rob H Rob Murray Ronen Ness ronrest Roy Iacob rrao rrawat Ryan Smith ryanyuyu Ry Sachin Kalkur sagism Saiful Azad Sam Krygsheld Sam Whited Sangeeth Sudheer Saqib Shamsi Sardathrion Sardorbek Imomaliev sarvajeetsuman SashaZd satsumas sayan Scott Mermelstein Sebastian Schrader Selcuk Sempoo Serenity SerialDev serv Seth M. Larson Seven sevenforce ShadowRanger Shantanu Alshi Shawn Mehan Shihab Shahriar Shijo Shoe Shrey Gupta Shreyash S Sarnayak Shuo SiggyF Simon Fraser Simon Hibbs Simplans Sirajus Salayhin sisanared skrrgwasme SN Ravichandran KR solarc

Chapter 21 Chapter 121 Chapter 94 Chapter 30 Chapters 19 and 41 Chapter 19 Chapter 1 Chapter 161 Chapters 21 and 120 Chapter 21 Chapter 149 Chapter 125 Chapter 5 Chapter 43 Chapter 1 Chapter 111 Chapter 1 Chapter 92 Chapter 103 Chapter 103 Chapters 9 and 16 Chapters 12, 14, 29, 64, 86, 134, 143, 144, 146 and 194 Chapter 67 Chapters 1 and 96 Chapters 110, 135 and 149 Chapter 173 Chapters 1, 28, 149 and 201 Chapter 38 Chapters 20, 30, 40, 43, 149 and 173 Chapter 82 Chapter 40 Chapters 54 and 87 Chapters 1, 11, 20 and 33 Chapters 16 and 67 Chapter 149 Chapter 173 Chapters 10, 15, 19, 20 and 47 Chapter 200 Chapter 198 Chapter 21 Chapters 41, 56 and 173 Chapter 12 Chapter 77 Chapters 16 and 111 Chapter 173 Chapter 169 Chapters 1, 9, 10, 14, 16, 19, 21, 28, 33, 37, 38, 41, 43, 44, 45, 50, 69, 75, 77, 82, 88, 99, 147, 149 and 173 Chapters 5, 175 and 176 Chapter 39 Chapters 16 and 99 Chapter 75 Chapters 20 and 149

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Soumendra Kumar Sahoo SouvikMaji sricharan StardustGogeta stark Stephen Nyamweya Steve Barnes Steven Maude sth strpeter StuxCrystal Sudip Bhandari Sun Qingyao Sunny Patel SuperBiasedMan supersam654 surfthecity Symmitchry sytech Sнаđошƒа Tadhg McDonald talhasch Tasdik Rahman taylor swift techydesigner Teepeemm Tejas Jadhav Tejus Prasad TemporalWolf textshell TheGenie OfTruth theheadofabroom the_cat_lady The_Curry_Man Thomas Ahle Thomas Crowley Thomas Gerot Thomas Moreau Thtu Tim Tim D tjohnson tlo tobias_k Tom Tom Barron Tom de Geus Tony Meyer Tony Suffolk 66 tox123 TuringTux Tyler Crompton

Chapters 14 and 38 Chapter 1 Chapter 149 Chapter 43 Chapter 1 Chapter 161 Chapters 33 and 82 Chapters 11, 33, 47 and 92 Chapters 91, 92 and 141 Chapters 85 and 192 Chapters 21, 37, 43, 56, 76, 82, 121 and 142 Chapter 88 Chapter 101 Chapter 21 Chapters 1, 4, 15, 16, 20, 21, 26, 29, 30, 33, 41, 43, 64, 69, 77, 80, 88, 132, 149 and 200 Chapter 70 Chapter 6 Chapters 47 and 53 Chapter 92 Chapters 1 and 134 Chapter 149 Chapters 92, 93 and 164 Chapter 30 Chapter 1 Chapters 1, 38, 43 and 190 Chapter 152 Chapter 201 Chapter 1 Chapter 90 Chapters 16, 28, 33 and 121 Chapter 1 Chapters 41, 49 and 64 Chapter 43 Chapter 16 Chapters 60 and 134 Chapter 105 Chapters 30, 43, 58, 61, 126 and 181 Chapter 119 Chapter 80 Chapter 149 Chapter 200 Chapter 44 Chapter 82 Chapters 28 and 40 Chapter 16 Chapters 1 and 21 Chapter 1 Chapter 43 Chapters 9, 10, 38 and 40 Chapter 38 Chapter 4 Chapter 86

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Chapters 119 and 122 Chapter 54 Chapter 38 Chapter 30 Chapter 49 Chapter 9 Chapter 116 Chapter 163 Chapters 8, 9, 13, 16, 20, 28, 30, 33, 38, 40, 41, 57, 60, 64, 69, 75, 88, 110, 134, 142, 149, 154, 161, 196 and 200 Chapters 43 and 187 Chapter 166 Chapter 1 Chapters 1, 40 and 77 Chapter 33 Chapter 33 Chapter 20 Chapter 20 Chapter 1 Chapter 161 Chapters 20, 21, 43, 110 and 149 Chapter 43 Chapters 20, 33, 43 and 77 Chapters 21, 33, 41 and 110 Chapter 174 Chapters 1 and 17 Chapter 149 Chapters 99, 116 and 120 Chapters 10 and 19 Chapter 31 Chapter 103 Chapter 1 Chapters 41 and 149 Chapter 53 Chapters 5, 15, 16, 21, 30, 33, 91, 116, 117, 121 and 164 Chapter 155 Chapters 21, 37 and 197 Chapters 41, 43 and 53 Chapter 149 Chapter 30 Chapter 200 Chapter 173 Chapter 14 Chapter 9 Chapters 15, 19, 111 and 120 Chapter 47 Chapters 14 and 30 Chapter 52 Chapters 67 and 149 Chapter 85 Chapter 190 Chapters 1 and 117 Chapter 64

GoalKicker.com – Python® Notes for Professionals

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Chapter 1 Chapter 1 Chapter 67 Chapter 149 Chapter 21 Chapter 34 Chapter 145 Chapters 83, 140 and 141 Chapters 75 and 83 Chapter 173 Chapters 13, 37, 39 and 112 Chapter 33 Chapter 81 Chapter 117 Chapters 67 and 77 Chapters 24, 37 and 128

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